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1
Zang, Hao. "Non-redundant sequential association rule mining based on closed sequential patterns." Thesis, Queensland University of Technology, 2010. https://eprints.qut.edu.au/46166/1/Hao_Zang_Thesis.pdf.
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In many applications, e.g., bioinformatics, web access traces, system utilisation logs, etc., the data is naturally in the form of sequences. People have taken great interest in analysing the sequential data and finding the inherent characteristics or relationships within the data. Sequential association rule mining is one of the possible methods used to analyse this data. As conventional sequential association rule mining very often generates a huge number of association rules, of which many are redundant, it is desirable to find a solution to get rid of those unnecessary association rules. Because of the complexity and temporal ordered characteristics of sequential data, current research on sequential association rule mining is limited. Although several sequential association rule prediction models using either sequence constraints or temporal constraints have been proposed, none of them considered the redundancy problem in rule mining. The main contribution of this research is to propose a non-redundant association rule mining method based on closed frequent sequences and minimal sequential generators. We also give a definition for the non-redundant sequential rules, which are sequential rules with minimal antecedents but maximal consequents. A new algorithm called CSGM (closed sequential and generator mining) for generating closed sequences and minimal sequential generators is also introduced. A further experiment has been done to compare the performance of generating non-redundant sequential rules and full sequential rules, meanwhile, performance evaluation of our CSGM and other closed sequential pattern mining or generator mining algorithms has also been conducted. We also use generated non-redundant sequential rules for query expansion in order to improve recommendations for infrequently purchased products.
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Rahman, Anisur. "Rare sequential pattern mining of critical infrastructure control logs for anomaly detection." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/132077/1/__qut.edu.au_Documents_StaffHome_staffgroupW%24_wu75_Documents_ePrints_Anisur_Rahman_Thesis_Redacted.pdf.
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Supervisory Control and Data Acquisition (SCADA) systems are used to drive much of a nation's critical infrastructure, which by definition is essential for the nation's citizens' way of life. They are connected to the computer networks and internet systems to operate, control and monitor their operations. This connectivity enables these SCADA systems to be exposed to cyber-attacks. This thesis detects anomalies or cyber-attacks on SCADA systems. It analyses SCADA control logs to find abnormal process activities which are treated as anomalies. A novel rare sequential pattern mining approach is proposed and developed to find rare or abnormal behaviour in SCADA systems.
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Abar, Orhan. "Rule Mining and Sequential Pattern Based Predictive Modeling with EMR Data." UKnowledge, 2019. https://uknowledge.uky.edu/cs_etds/85.
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Electronic medical record (EMR) data is collected on a daily basis at hospitals and other healthcare facilities to track patients’ health situations including conditions, treatments (medications, procedures), diagnostics (labs) and associated healthcare operations. Besides being useful for individual patient care and hospital operations (e.g., billing, triaging), EMRs can also be exploited for secondary data analyses to glean discriminative patterns that hold across patient cohorts for different phenotypes. These patterns in turn can yield high level insights into disease progression with interventional potential. In this dissertation, using a large scale realistic EMR dataset of over one million patients visiting University of Kentucky healthcare facilities, we explore data mining and machine learning methods for association rule (AR) mining and predictive modeling with mood and anxiety disorders as use-cases. Our first work involves analysis of existing quantitative measures of rule interestingness to assess how they align with a practicing psychiatrist’s sense of novelty/surprise corresponding to ARs identified from EMRs. Our second effort involves mining causal ARs with depression and anxiety disorders as target conditions through matching methods accounting for computationally identified confounding attributes. Our final effort involves efficient implementation (via GPUs) and application of contrast pattern mining to predictive modeling for mental conditions using various representational methods and recurrent neural networks. Overall, we demonstrate the effectiveness of rule mining methods in secondary analyses of EMR data for identifying causal associations and building predictive models for diseases.
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Wu, Sheng-Tang. "Knowledge discovery using pattern taxonomy model in text mining." Queensland University of Technology, 2007. http://eprints.qut.edu.au/16675/.
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In the last decade, many data mining techniques have been proposed for fulfilling various knowledge discovery tasks in order to achieve the goal of retrieving useful information for users. Various types of patterns can then be generated using these techniques, such as sequential patterns, frequent itemsets, and closed and maximum patterns. However, how to effectively exploit the discovered patterns is still an open research issue, especially in the domain of text mining. Most of the text mining methods adopt the keyword-based approach to construct text representations which consist of single words or single terms, whereas other methods have tried to use phrases instead of keywords, based on the hypothesis that the information carried by a phrase is considered more than that by a single term. Nevertheless, these phrase-based methods did not yield significant improvements due to the fact that the patterns with high frequency (normally the shorter patterns) usually have a high value on exhaustivity but a low value on specificity, and thus the specific patterns encounter the low frequency problem. This thesis presents the research on the concept of developing an effective Pattern Taxonomy Model (PTM) to overcome the aforementioned problem by deploying discovered patterns into a hypothesis space. PTM is a pattern-based method which adopts the technique of sequential pattern mining and uses closed patterns as features in the representative. A PTM-based information filtering system is implemented and evaluated by a series of experiments on the latest version of the Reuters dataset, RCV1. The pattern evolution schemes are also proposed in this thesis with the attempt of utilising information from negative training examples to update the discovered knowledge. The results show that the PTM outperforms not only all up-to-date data mining-based methods, but also the traditional Rocchio and the state-of-the-art BM25 and Support Vector Machines (SVM) approaches.
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Wu, Sheng-Tang. "Knowledge discovery using pattern taxonomy model in text mining." Thesis, Queensland University of Technology, 2007. https://eprints.qut.edu.au/16675/1/Sheng-Tang_Wu_Thesis.pdf.
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In the last decade, many data mining techniques have been proposed for fulfilling various knowledge discovery tasks in order to achieve the goal of retrieving useful information for users. Various types of patterns can then be generated using these techniques, such as sequential patterns, frequent itemsets, and closed and maximum patterns. However, how to effectively exploit the discovered patterns is still an open research issue, especially in the domain of text mining. Most of the text mining methods adopt the keyword-based approach to construct text representations which consist of single words or single terms, whereas other methods have tried to use phrases instead of keywords, based on the hypothesis that the information carried by a phrase is considered more than that by a single term. Nevertheless, these phrase-based methods did not yield significant improvements due to the fact that the patterns with high frequency (normally the shorter patterns) usually have a high value on exhaustivity but a low value on specificity, and thus the specific patterns encounter the low frequency problem.
This thesis presents the research on the concept of developing an effective Pattern Taxonomy Model (PTM) to overcome the aforementioned problem by deploying discovered patterns into a hypothesis space. PTM is a pattern-based method which adopts the technique of sequential pattern mining and uses closed patterns as features in the representative. A PTM-based information filtering system is implemented and evaluated by a series of experiments on the latest version of the Reuters dataset, RCV1. The pattern evolution schemes are also proposed in this thesis with the attempt of utilising information from negative training examples to update the discovered knowledge. The results show that the PTM outperforms not only all up-to-date data mining-based methods, but also the traditional Rocchio and the state-of-the-art BM25 and Support Vector Machines (SVM) approaches.
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Algarni, Abdulmohsen. "Relevance feature discovery for text analysis." Thesis, Queensland University of Technology, 2011. https://eprints.qut.edu.au/48230/1/Abdulmohsen_Algarni_Thesis.pdf.
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It is a big challenge to guarantee the quality of discovered relevance features in text documents for describing user preferences because of the large number of terms, patterns, and noise. Most existing popular text mining and classification methods have adopted term-based approaches. However, they have all suffered from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern-based methods should perform better than term- based ones in describing user preferences, but many experiments do not support this hypothesis. This research presents a promising method, Relevance Feature Discovery (RFD), for solving this challenging issue. It discovers both positive and negative patterns in text documents as high-level features in order to accurately weight low-level features (terms) based on their specificity and their distributions in the high-level features. The thesis also introduces an adaptive model (called ARFD) to enhance the exibility of using RFD in adaptive environment. ARFD automatically updates the system's knowledge based on a sliding window over new incoming feedback documents. It can efficiently decide which incoming documents can bring in new knowledge into the system. Substantial experiments using the proposed models on Reuters Corpus Volume 1 and TREC topics show that the proposed models significantly outperform both the state-of-the-art term-based methods underpinned by Okapi BM25, Rocchio or Support Vector Machine and other pattern-based methods.
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João, Rafael Stoffalette. "Mineração de padrões sequenciais e geração de regras de associação envolvendo temporalidade." Universidade Federal de São Carlos, 2015. https://repositorio.ufscar.br/handle/ufscar/8923.
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Previous issue date: 2015-05-07<br>Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)<br>Data mining aims at extracting useful information from a Database (DB). The mining
process enables, also, to analyze the data (e.g. correlations, predictions, chronological
relationships, etc.). The work described in this document proposes an approach to deal with
temporal knowledge extraction from a DB and describes the implementation of this
approach, as the computational system called S_MEMIS+AR. The system focuses on the
process of finding frequent temporal patterns in a DB and generating temporal association
rules, based on the elements contained in the frequent patterns identified. At the end of the
process performs an analysis of the temporal relationships between time intervals
associated with the elements contained in each pattern using the binary relationships
described by the Allen´s Interval Algebra. Both, the S_MEMISP+AR and the algorithm that
the system implements, were subsidized by the Apriori, the MEMISP and the ARMADA
approaches. Three experiments considering two different approaches were conducted with
the S_MEMISP+AR, using a DB of sale records of products available in a supermarket.
Such experiments were conducted to show that each proposed approach, besides inferring
new knowledge about the data domain and corroborating results that reinforce the implicit
knowledge about the data, also promotes, in a global way, the refinement and extension of
the knowledge about the data.<br>A mineração de dados tem como objetivo principal a extração de informações úteis a partir
de uma Base de Dados (BD). O processo de mineração viabiliza, também, a realização de
análises dos dados (e.g, identificação de correlações, predições, relações cronológicas,
etc.). No trabalho descrito nesta dissertação é proposta uma abordagem à extração de
conhecimento temporal a partir de uma BD e detalha a implementação dessa abordagem
por meio de um sistema computacional chamado S_MEMISP+AR. De maneira simplista, o
sistema tem como principal tarefa realizar uma busca por padrões temporais em uma base
de dados, com o objetivo de gerar regras de associação temporais entre elementos de
padrões identificados. Ao final do processo, uma análise das relações temporais entre os
intervalos de duração dos elementos que compõem os padrões é feita, com base nas
relações binárias descritas pelo formalismo da Álgebra Intervalar de Allen. O sistema
computacional S_MEMISP+AR e o algoritmo que o sistema implementa são subsidiados
pelas propostas Apriori, ARMADA e MEMISP. Foram realizados três experimentos distintos,
adotando duas abordagens diferentes de uso do S_MEMISP+AR, utilizando uma base de
dados contendo registros de venda de produtos disponibilizados em um supermercado. Tais
experimentos foram apresentados como forma de evidenciar que cada uma das
abordagens, além de inferir novo conhecimento sobre o domínio de dados e corroborar
resultados que reforçam o conhecimento implícito já existente sobre os dados, promovem,
de maneira global, o refinamento e extensão do conhecimento sobre os dados.
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Carvalho, Danilo Codeco. "Obtenção de padrões sequenciais em data streams atendendo requisitos do Big Data." Universidade Federal de São Carlos, 2016. https://repositorio.ufscar.br/handle/ufscar/8280.
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Previous issue date: 2016-06-06<br>Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)<br>The growing amount of data produced daily, by both businesses and individuals in the web, increased the demand for analysis and extraction of knowledge of this data. While the last two decades the solution was to store and perform data mining algorithms, currently it has become unviable even to supercomputers. In addition, the requirements of the Big Data age go far beyond the large amount of data to analyze. Response time requirements and complexity of the data acquire more weight in many areas in the real world. New models have been researched and developed, often proposing distributed computing or different ways to handle the data stream mining. Current researches shows that an alternative in the data stream mining is to join a real-time event handling mechanism with a classic mining association rules or sequential patterns algorithms. In this work is shown a data stream mining approach to meet the Big Data response time requirement, linking the event handling mechanism in real time Esper and Incremental Miner of Stretchy Time Sequences (IncMSTS) algorithm. The results show that is possible to take a static data mining algorithm for data stream environment and keep tendency
in the patterns, although not possible to continuously read all data coming into the data stream.<br>O crescimento da quantidade de dados produzidos diariamente, tanto por empresas como por indivíduos na web, aumentou a exigência para a análise e extração de conhecimento sobre esses dados. Enquanto nas duas últimas décadas a solução era armazenar e executar algoritmos de mineração de dados, atualmente isso se tornou inviável mesmo em super computadores. Além disso, os requisitos da chamada era do Big Data vão muito além da grande quantidade de dados a se analisar. Requisitos de tempo de resposta e complexidade dos dados adquirem maior peso em muitos domínios no mundo real. Novos modelos têm sido pesquisados e desenvolvidos, muitas vezes propondo computação distribuída ou diferentes formas de se tratar a mineração de fluxo de dados. Pesquisas atuais mostram que uma alternativa na mineração de fluxo de dados é unir um mecanismo de tratamento de eventos em tempo real com algoritmos clássicos de mineração de regras de associação ou padrões sequenciais. Neste trabalho é mostrada uma abordagem de mineração de fluxo de dados (data stream) para atender ao requisito de tempo de resposta do Big Data, que une o mecanismo de manipulação de eventos em tempo real Esper e o algoritmo Incremental Miner of Stretchy Time Sequences (IncMSTS). Os resultados mostram ser possível levar um algoritmo de mineração de dados estático para o ambiente de fluxo de dados e manter as tendências de padrões encontrados, mesmo não sendo possível ler todos os dados vindos continuamente no fluxo de dados.
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Cano, Marcos Daniel. "Mineração de regras de associação sequenciais em séries temporais e visualização: aplicação em dados agrometeorológicos." Universidade Federal de São Carlos, 2012. https://repositorio.ufscar.br/handle/ufscar/564.
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Previous issue date: 2012-08-03<br>Universidade Federal de Minas Gerais<br>Technological development brought improvements in the technology of climate sensors and Earth's surface image acquisition, gathering increasing amounts of data. Generally, when these data are submitted to mining algorithms, the output is the production of hundreds or even thousands of textual patterns, making the task of data analysis by the domain expert even harder. Hence, it is crucial, to support experts, the development of a tool that helps to identify and display patterns of interest. In this context, this research project at Master Science level aims to develop a technique for mining association rules in time series allowing agrometeorological data analysis over time.<br>O avanço tecnológico tem propiciado melhorias nos diversos sensores utilizados para medições dos dados climáticos e de imageamento da superfície terrestre, coletando quantidades cada vez maiores de dados. Quando esses dados são submetidos aos algoritmos de mineração para serem explorados ocorre, em geral, a produção de centenas ou ate mesmo milhares de padrões textuais, dificultando ainda mais a tarefa de analise dos dados pelo especialista de domínio. Assim, e crucial, para apoiar os especialistas, o desenvolvimento de um ferramental que auxilia na identificação e visualização dos padrões de interesse. Neste contexto, este projeto de pesquisa em nível de mestrado visa desenvolver uma técnica de mineração de regras de associação em series temporais permitindo a analise de dados agrometeorológicos ao longo do tempo.
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Cai, Chun Hing. "Mining association rules with weighted items." Hong Kong : Chinese University of Hong Kong, 1998. http://www.cse.cuhk.edu.hk/%7Ekdd/assoc%5Frule/thesis%5Fchcai.pdf.
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Thesis (M. Phil.)--Chinese University of Hong Kong, 1998.<br>Description based on contents viewed Mar. 13, 2007; title from title screen. Includes bibliographical references (p. 99-103). Also available in print.
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Zhou, Zequn. "Maintaining incremental data mining association rules." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/MQ62311.pdf.
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Goulbourne, Graham. "Tree algorithms for mining association rules." Thesis, University of Liverpool, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.250218.
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With the increasing reliability of digital communication, the falling cost of hardware and increased computational power, the gathering and storage of data has become easier than at any other time in history. Commercial and public agencies are able to hold extensive records about all aspects of their operations. Witness the proliferation of point of sale (POS) transaction recording within retailing, digital storage of census data and computerized hospital records. Whilst the gathering of such data has uses in terms of answering specific queries and allowing visulisation of certain trends the volumes of data can hide significant patterns that would be impossible to locate manually. These patterns, once found, could provide an insight into customer behviour, demographic shifts and patient diagnosis hitherto unseen and unexpected. Remaining competitive in a modem business environment, or delivering services in a timely and cost effective manner for public services is a crucial part of modem economics. Analysis of the data held by an organisaton, by a system that "learns" can allow predictions to be made based on historical evidence. Users may guide the process but essentially the software is exploring the data unaided. The research described within this thesis develops current ideas regarding the exploration of large data volumes. Particular areas of research are the reduction of the search space within the dataset and the generation of rules which are deduced from the patterns within the data. These issues are discussed within an experimental framework which extracts information from binary data.
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Koh, Yun Sing, and n/a. "Generating sporadic association rules." University of Otago. Department of Computer Science, 2007. http://adt.otago.ac.nz./public/adt-NZDU20070711.115758.
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Association rule mining is an essential part of data mining, which tries to discover associations, relationships, or correlations among sets of items. As it was initially proposed for market basket analysis, most of the previous research focuses on generating frequent patterns. This thesis focuses on finding infrequent patterns, which we call sporadic rules. They represent rare itemsets that are scattered sporadically throughout the database but with high confidence of occurring together. As sporadic rules have low support the minabssup (minimum absolute support) measure was proposed to filter out any rules with low support whose occurrence is indistinguishable from that of coincidence. There are two classes of sporadic rules: perfectly sporadic and imperfectly sporadic rules. Apriori-Inverse was then proposed for perfectly sporadic rule generation. It uses a maximum support threshold and user-defined minimum confidence threshold. This method is designed to find itemsets which consist only of items falling below a maximum support threshold. However imperfectly sporadic rules may contain items with a frequency of occurrence over the maximum support threshold. To look for these rules, variations of Apriori-Inverse, namely Fixed Threshold, Adaptive Threshold, and Hill Climbing, were proposed. However these extensions are heuristic. Thus the MIISR algorithm was proposed to find imperfectly sporadic rules using item constraints, which capture rules with a single-item consequent below the maximum support threshold. A comprehensive evaluation of sporadic rules and current interestingness measures was carried out. Our investigation suggests that current interestingness measures are not suitable for detecting sporadic rules.
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王漣 and Lian Wang. "A study on quantitative association rules." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1999. http://hub.hku.hk/bib/B31223588.
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Wang, Lian. "A study on quantitative association rules /." Hong Kong : University of Hong Kong, 1999. http://sunzi.lib.hku.hk/hkuto/record.jsp?B2118561X.
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Zhu, Hua. "On-line analytical mining of association rules." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ37678.pdf.
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Delpisheh, Elnaz, and University of Lethbridge Faculty of Arts and Science. "Two new approaches to evaluate association rules." Thesis, Lethbridge, Alta. : University of Lethbridge, Dept. of Mathematics and Computer Science, c2010, 2010. http://hdl.handle.net/10133/2530.
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Data mining aims to discover interesting and unknown patterns in large-volume data. Association rule mining is one of the major data mining tasks, which attempts to find inherent relationships among data items in an application domain, such as supermarket basket analysis. An essential post-process in an association rule mining task is the evaluation of association rules by measures for their interestingness. Different interestingness measures have been proposed and studied. Given an association rule mining task, measures are assessed against a set of user-specified properties. However, in practice, given the subjectivity and inconsistencies in property specifications, it is a non-trivial task to make appropriate measure selections. In this work, we propose two novel approaches to assess interestingness measures. Our first approach utilizes the analytic hierarchy process to capture quantitatively domain-dependent requirements on properties, which are later used in assessing measures. This approach not only eliminates any inconsistencies in an end user’s property specifications through consistency checking but also is invariant to the number of association rules. Our
second approach dynamically evaluates association rules according to a composite and
collective effect of multiple measures. It interactively snapshots the end user’s domain-
dependent requirements in evaluating association rules. In essence, our approach uses
neural networks along with back-propagation learning to capture the relative importance
of measures in evaluating association rules. Case studies and simulations have been conducted to show the effectiveness of our two approaches.<br>viii, 85 leaves : ill. ; 29 cm
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魯建江 and Kin-kong Loo. "Efficient mining of association rules using conjectural information." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2001. http://hub.hku.hk/bib/B31224878.
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Loo, Kin-kong. "Efficient mining of association rules using conjectural information." Hong Kong : University of Hong Kong, 2001. http://sunzi.lib.hku.hk/hkuto/record.jsp?B22505544.
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Wu, Jingtong. "Interpretation of association rules with multi-tier granule mining." Thesis, Queensland University of Technology, 2014. https://eprints.qut.edu.au/71455/1/Jing_Wu_Thesis.pdf.
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This study was a step forward to improve the performance for discovering useful knowledge – especially, association rules in this study – in databases. The thesis proposed an approach to use granules instead of patterns to represent knowledge implicitly contained in relational databases; and multi-tier structure to interpret association rules in terms of granules. Association mappings were proposed for the construction of multi-tier structure. With these tools, association rules can be quickly assessed and meaningless association rules can be justified according to the association mappings. The experimental results indicated that the proposed approach is promising.
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Pray, Keith A. "Apriori Sets And Sequences: Mining Association Rules from Time Sequence Attributes." Link to electronic thesis, 2004. http://www.wpi.edu/Pubs/ETD/Available/etd-0506104-150831/.
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Thesis (M.S.) -- Worcester Polytechnic Institute.<br>Keywords: mining complex data; temporal association rules; computer system performance; stock market analysis; sleep disorder data. Includes bibliographical references (p. 79-85).
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An, Pao-Ying, and 安寶楹. "Mining Associative Sequential Rules for Image Classification." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/96293014152058508425.
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碩士<br>國立臺灣師範大學<br>資訊教育研究所<br>90<br>In this thesis, a new image classification method based on mining associative sequential rules is proposed.
First, the colour blocks in an image is extracted. Moreover, the attribute values of the colour blocks are recorded, including the area, x-position, y-position of the color block and so on.
A colour block with a specific colour is defined as an image feature term.The extracted colour blocks are sorted according to a colour attribute to form a sequence of image feature terms, which is the data used to represent the characteristic of an image.
Moreover, an efficient sequential pattern mining algorithm is provided. The frequent sequential patterns are mined from the sequences of image feature terms extracted from training images to derive associative classification rules. The data structures “bits index table” and “appearing index table” are designed to assist mining frequent sequential patterns and classification rules quickly.
Finally, the judgement method of classification is designed based on multiple classification rules instead of one single rule.
The experiments are performed on natural images and animal images obtained from Corel Gallery CD. The results show that the average accurate rate of image classification, achieved by the method proposed in this thesis, is above 92%. In addition, the performance of accurate rate of our method is better than the related works.
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Lin, Ming-Yen, and 林明言. "Efficient Algorithms for Association Rule Mining and Sequential Pattern Mining." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/m8z62p.
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博士<br>國立交通大學<br>資訊工程系所<br>92<br>Tremendous amount of data being collected is increasing speedily by
computerized applications around the world. Hidden in the vast data, the
valuable information is attracting researchers of multiple disciplines to
study effective approaches to derive useful knowledge from within. Among
various data mining objectives, the mining of frequent patterns has been the
focus of knowledge discovery in databases. This thesis aims to investigate
efficient algorithms for mining frequent patterns including association rules
and sequential patterns.
We propose the LexMiner algorithm to deal with frequent item-set discovery for
association rules. To alleviate the drawbacks of hash-tree placement of
candidates, some algorithms store candidate patterns according to prefix-order
of itemsets. LexMiner utilizes the lexicographic features and lexicographic
comparisons to further speed up the kernel operation of mining algorithms.
A memory indexing approach called MEMISP is proposed for fast sequential
pattern mining using a find-then-index technique. MEMISP mines databases of
any size, with respect to any support threshold, in just two passes of
database scanning. MEMISP outperforms other algorithms in that neither
candidate patterns nor intermediate databases are generated.
Mining sequential patterns with time constraints, such as time gaps and
sliding time-window, may reinforce the accuracy of mining results. However,
the capabilities to mine the time-constrained patterns were previously
available only within Apriori framework. Recent studies indicate that pattern-
growth methodology could speed up sequence mining. We integrate the constraints
into a divide-and-conquer strategy of sub-database projection and propose the
pattern-growth based DELISP algorithm, which outperforms other algorithms in
mining time-constrained sequential patterns.
In practice, knowledge discovery is an iterative process. Thus, reducing the
response time during user interactions for the desired outcome is crucial. The
proposed KISP algorithm utilizes the knowledge acquired from individual mining
process, accumulates the counting information to facilitate efficient counting
of patterns, and accelerates the whole interactive sequence mining process.
Current approaches for sequential pattern mining usually assume that the
mining is performed with respect to a static sequence database. However,
databases are not static due to update so that the discovered patterns might
become invalid and new patterns could be created. Instead of re-mining from
scratch, the proposed IncSP algorithm solves the incremental update problem
through effective implicit merging and efficient separate counting over
appended sequences. Patterns found in prior stages are incrementally updated
rather than re-mining.
Comprehensive experiments have been conducted to assess the performance of
the proposed algorithms. The empirical results show that these algorithms
outperform state-of-the-art algorithms with respect to various mining
parameters and datasets of different characteristics. The scale-up experiments
also verify that our algorithms successfully mine frequent patterns with good
linear scalability.
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Lin, Kuei Ying, and 林桂英. "Mining Fuzzy Multiple-level Association Rules and Fuzzy Sequential Patterns from Quantitative Data." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/83878981510147486751.
Full textAbstract:
碩士<br>義守大學<br>資訊工程學系<br>89<br>Many researchers in database and machine learning fields are primarily interested in data mining because it offers opportunities to discover useful information and important relevant patterns in large databases. Most previous studies have shown how binary valued transaction data may be handled. Transaction data in real-world applications usually consist of quantitative values, so designing a sophisticated data-mining algorithm able to deal with various types of data presents a challenge to workers in this research field.
This paper thus proposes two kinds of fuzzy mining algorithms, respectively for multiple-level association rules and sequential patterns, to extract knowledge implicit in transactions stored as quantitative values. The proposed fuzzy mining algorithms first transform quantitative values in transactions into linguistic terms, then filter them to find fuzzy association rules or sequential patterns by modifying the conventional mining algorithms. Each quantitative item uses only the linguistic term with the maximum cardinality or uses all possible linguistic terms in the mining processes. If only the linguistic terms with the maximum cardinalities are used, the number of fuzzy regions to be processed is the same as that of the original items. The algorithms therefore focus on the most important linguistic terms and reduce their time complexity. If all linguistic terms are used in the mining process, the derived set of rules or patterns is more complete, although computation is more complex. In addition, a web mining algorithm for fuzzy browsing patterns from the world wide web has also been proposed. The association rules and sequential patterns mined out thus exhibit important quantitative regularity in databases and can be used to provide some suggestions to appropriate supervisors.
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Chiu, Chia-Wei, and 邱佳偉. "Mining Web Browsing Behavior by the Association Rule and the Sequential Pattern." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/4yw577.
Full textAbstract:
碩士<br>國立臺北科技大學<br>商業自動化與管理研究所<br>96<br>Rich connected links and information on the internet have changed enterprises’ ways to do business. Internet also enlarges the field of marketing and forms many creative business models, such as B2B, B2C, C2B, and C2C. Nowadays, enterprises can access more potential customers through broadcasting product and brand information by internet. As a result, how to build an automatic system to help enterprises find out valuable customers and understand customer’s online behaviors is an extremely important issue now. This research is to build a systematic mechanism to find out valuable customers and understand their online browsing and shopping behaviors.
RFM model, the association rule and the sequential pattern method are adopted to mine the internet clickstream data of a B2C e-commerce website. The empirical results show our mechanism can well achieve our research purpose.
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Chen, Wei-Ren, and 陳威任. "Mining Utility Association Rules." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/04865121871313091524.
Full textAbstract:
碩士<br>銘傳大學<br>資訊工程學系碩士班<br>103<br>Mining Association Rules can find which products would be purchased by the customer when a customer has bought some products, and we can use association rules to recommend products for customers. Mining High Utility Itemset is to find the combinations of products which could bring high profit to us. However, High Utility Itemset only tells us which products bring high profit but not increase profit when we recommend other product to customer. Therefore, we propose definitions and algorithm of Mining Utility Association Rules to find which product to recommend and to bring us more benefit than the original high utility itemsets. We will clearly know which product should be recommended to customer bring more profit to us with Utility Association Rules.
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Li, Li-Ya, and 李立雅. "Inter-sequence Association Rules Mining." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/07341599579887641679.
Full textAbstract:
碩士<br>國立臺灣大學<br>資訊管理研究所<br>91<br>There are many algorithms proposed to find sequential patterns in sequence databases where each transaction contains one sequence. Previously proposed algorithms treat each sequence as an independent one. This kind of mining belongs to intra-transaction sequential patterns mining.
In this paper, we propose an algorithm, ProbSif, to mine inter-sequence association rules. Our proposed algorithm consists of three phases. First, we find all large intra-sequence patterns. For each large pattern found, all the time points at which the pattern occurs are recorded in a time point list. Second, those time point lists are hashed into L-buckets. Third, we use a level-wise candidate generation-and-test method to generate candidate patterns across different sequences and check if a candidate is large. Once we generate a candidate, we count its support by reading relevant time point lists from L-buckets. By using the L-buckets, our proposed algorithm requires fewer database scans than the Apriori-like approach. Therefore, our proposed algorithm is more efficient. The experimental results show that our proposed algorithm outperforms the Apriori-like approach by several orders of magnitude.
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Chang, Paul C. M., and 張仲銘. "Mining Association Rules by Sorts." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/27186430188696978772.
Full textAbstract:
碩士<br>國立清華大學<br>資訊工程學系<br>86<br>In this thesis, we use the knowledge about the sorts of items
and transactions to discover association rules among items in a
market transaction database. It is natural to divide items into
sorts: milk and bread belong to the sort of food while gloves
and hats pertain to the sort of clothing. We sort each
transaction according to the sorts of items contained by this
transaction. Then each sort of transactions will form a subset
of the entire database. To discover the association rules within
and between these subsets, two kinds of support-constraint
models with the corresponding algorithms are proposed. We claim
that such models not only enrich the semantics of rules compared
with the inceptive work but also emphasize the customer buying
patterns for both intra-sort and inter-sort merchandise. The
constraint needed when generating rules based on sorts of items
is also discussed. The experiments evaluate the performance of
these algorithms on synthetical databases of different inter-
sort patterns.
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Su, Wei-Tu, and 蘇威圖. "Mining Multidimensional Intertransaction Association Rules." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/54923326716084839179.
Full textAbstract:
碩士<br>國立臺灣大學<br>資訊管理研究所<br>90<br>Traditionally, association rule data mining almost focuses on finding the associations among items within the same transaction. In this thesis, we explore “Multidimensional Intertrnasaction Association Rules”, which tries to find the association rule from different transactions and extend to multidimensional space. We propose the E-Partition algorithm and use the Grid File as our data structure to find the large itemsets in the database. Besides, we propose the E-DELTA algorithm to deal with the incremental data mining.
The experiment shows that the E-Partition algorithm performs better than the E-Apriori algorithm. Also, the algorithm using the Grid File has better efficiency than that scanning database does.
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Yang, Chian-Yi, and 楊千儀. "Mining High Utility Quantitative Association Rules." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/2jdtaf.
Full textAbstract:
碩士<br>銘傳大學<br>資訊傳播工程學系碩士班<br>94<br>Mining weighted association rules consider the importance of items in a large transaction database. Mining quantitative association rules find most quantitative itemsets , which are purchased frequently, and relate with them from a large transaction database. However, weighted association rules didn’t consider the items which their quantities, and quantitative association rules didn’t consider the items which their weighted. Economics mention influence that quantities affect the cost; and high prices are not necessarily to make a profit, that proves, if only consider weighted or quantitative, it’s must not enough. This paper will consider both weighted and quantitative, and find out useful rules for policymaker. We will weight of items multiply quantitative of items, it’s mean utility, and we want to find high utility association rules that these items reach to the utility threshold. Our methods don’t produce candidates and just scan once database to produce about sub-database, then we use these sub-database to find profitable association rules.
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Yi-Ling, Chen. "Mining Spatial Association Rules in Image." 2005. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0001-0907200516580400.
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Su, Po-Ta, and 蘇伯達. "Mining Association Rules by Ant System." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/51532702116386182507.
Full textAbstract:
碩士<br>國立清華大學<br>工業工程與工程管理學系<br>90<br>Mining association rules is to find relations among large amount of data so that the pattern of the dataset can be discovered. Many companies use association rules to find the relations among different items to improve their service quality of customers or enlarge their marketplace. Recently, many algorithms have been developed that only consider either non-quantitative data or quantitative data. However, in reality, most data we collected are mixed in types. Since Ant System allows to consider both of data types and has advantages of being efficient in filtering the unobvious association rules to reduce the unnecessary outputs and ease of making judgment to improve the performance, therefore, in this study, we adopted the technique and concept of Ant System to develop association rules. The developed algorithm is supported by theoretical evidence, and comparative studies are provided for evaluation.
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"Mining association rules with weighted items." 1998. http://library.cuhk.edu.hk/record=b5889513.
Full textAbstract:
by Cai, Chun Hing.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 1998.<br>Includes bibliographical references (leaves 109-114).<br>Abstract also in Chinese.<br>Acknowledgments --- p.ii<br>Abstract --- p.iii<br>Chapter 1 --- Introduction --- p.1<br>Chapter 1.1 --- Main Categories in Data Mining --- p.1<br>Chapter 1.2 --- Motivation --- p.3<br>Chapter 1.3 --- Problem Definition --- p.4<br>Chapter 1.4 --- Experimental Setup --- p.5<br>Chapter 1.5 --- Outline of the thesis --- p.6<br>Chapter 2 --- Literature Survey on Data Mining --- p.8<br>Chapter 2.1 --- Statistical Approach --- p.8<br>Chapter 2.1.1 --- Statistical Modeling --- p.9<br>Chapter 2.1.2 --- Hypothesis testing --- p.10<br>Chapter 2.1.3 --- Robustness and Outliers --- p.11<br>Chapter 2.1.4 --- Sampling --- p.12<br>Chapter 2.1.5 --- Correlation --- p.15<br>Chapter 2.1.6 --- Quality Control --- p.16<br>Chapter 2.2 --- Artificial Intelligence Approach --- p.18<br>Chapter 2.2.1 --- Bayesian Network --- p.19<br>Chapter 2.2.2 --- Decision Tree Approach --- p.20<br>Chapter 2.2.3 --- Rough Set Approach --- p.21<br>Chapter 2.3 --- Database-oriented Approach --- p.23<br>Chapter 2.3.1 --- Characteristic and Classification Rules --- p.23<br>Chapter 2.3.2 --- Association Rules --- p.24<br>Chapter 3 --- Background --- p.27<br>Chapter 3.1 --- Iterative Procedure: Apriori Gen --- p.27<br>Chapter 3.1.1 --- Binary association rules --- p.27<br>Chapter 3.1.2 --- Apriori Gen --- p.29<br>Chapter 3.1.3 --- Closure Properties --- p.30<br>Chapter 3.2 --- Introduction of Weights --- p.31<br>Chapter 3.2.1 --- Motivation --- p.31<br>Chapter 3.3 --- Summary --- p.32<br>Chapter 4 --- Mining weighted binary association rules --- p.33<br>Chapter 4.1 --- Introduction of binary weighted association rules --- p.33<br>Chapter 4.2 --- Weighted Binary Association Rules --- p.34<br>Chapter 4.2.1 --- Introduction --- p.34<br>Chapter 4.2.2 --- Motivation behind weights and counts --- p.36<br>Chapter 4.2.3 --- K-support bounds --- p.37<br>Chapter 4.2.4 --- Algorithm for Mining Weighted Association Rules --- p.38<br>Chapter 4.3 --- Mining Normalized Weighted association rules --- p.43<br>Chapter 4.3.1 --- Another approach for normalized weighted case --- p.45<br>Chapter 4.3.2 --- Algorithm for Mining Normalized Weighted Association Rules --- p.46<br>Chapter 4.4 --- Performance Study --- p.49<br>Chapter 4.4.1 --- Performance Evaluation on the Synthetic Database --- p.49<br>Chapter 4.4.2 --- Performance Evaluation on the Real Database --- p.58<br>Chapter 4.5 --- Discussion --- p.65<br>Chapter 4.6 --- Summary --- p.66<br>Chapter 5 --- Mining Fuzzy Weighted Association Rules --- p.67<br>Chapter 5.1 --- Introduction to the Fuzzy Rules --- p.67<br>Chapter 5.2 --- Weighted Fuzzy Association Rules --- p.69<br>Chapter 5.2.1 --- Problem Definition --- p.69<br>Chapter 5.2.2 --- Introduction of Weights --- p.71<br>Chapter 5.2.3 --- K-bound --- p.73<br>Chapter 5.2.4 --- Algorithm for Mining Fuzzy Association Rules for Weighted Items --- p.74<br>Chapter 5.3 --- Performance Evaluation --- p.77<br>Chapter 5.3.1 --- Performance of the algorithm --- p.77<br>Chapter 5.3.2 --- Comparison of unweighted and weighted case --- p.79<br>Chapter 5.4 --- Note on the implementation details --- p.81<br>Chapter 5.5 --- Summary --- p.81<br>Chapter 6 --- Mining weighted association rules with sampling --- p.83<br>Chapter 6.1 --- Introduction --- p.83<br>Chapter 6.2 --- Sampling Procedures --- p.84<br>Chapter 6.2.1 --- Sampling technique --- p.84<br>Chapter 6.2.2 --- Algorithm for Mining Weighted Association Rules with Sampling --- p.86<br>Chapter 6.3 --- Performance Study --- p.88<br>Chapter 6.4 --- Discussion --- p.91<br>Chapter 6.5 --- Summary --- p.91<br>Chapter 7 --- Database Maintenance with Quality Control method --- p.92<br>Chapter 7.1 --- Introduction --- p.92<br>Chapter 7.1.1 --- Motivation of using the quality control method --- p.93<br>Chapter 7.2 --- Quality Control Method --- p.94<br>Chapter 7.2.1 --- Motivation of using Mil. Std. 105D --- p.95<br>Chapter 7.2.2 --- Military Standard 105D Procedure [12] --- p.95<br>Chapter 7.3 --- Mapping the Database Maintenance to the Quality Control --- p.96<br>Chapter 7.3.1 --- Algorithm for Database Maintenance --- p.98<br>Chapter 7.4 --- Performance Evaluation --- p.102<br>Chapter 7.5 --- Discussion --- p.104<br>Chapter 7.6 --- Summary --- p.105<br>Chapter 8 --- Conclusion and Future Work --- p.106<br>Chapter 8.1 --- Summary of the Thesis --- p.106<br>Chapter 8.2 --- Conclusions --- p.107<br>Chapter 8.3 --- Future Work --- p.108<br>Bibliography --- p.108<br>Appendix --- p.115<br>Chapter A --- Generating a random number --- p.115<br>Chapter B --- Hypergeometric distribution --- p.116<br>Chapter C --- Quality control tables --- p.117<br>Chapter D --- Rules extracted from the database --- p.120
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Chen, Yi-Ling, and 陳奕伶. "Mining Spatial Association Rules in Image." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/39410529091384817730.
Full textAbstract:
碩士<br>國立臺灣大學<br>電機工程學研究所<br>93<br>In this paper, we integrate data mining with image processing for discovering spatial relationships in images. We present an image mining framework, Spatial Association Rulemining (SAR), to mine spatial associations located in specific locations of images. A rule in the SAR refers to the occurrences of image content in a pair of spatial locations. The proposed approach is applied to mine color spatial association rules (color-SAR) in landscape scene images so as to demonstrate that the spatial association rules is able to the application of image classification. Our experimental results show that the classification accuracy of 86% can be achieved by the rule-based classifier.
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Huang, Minghua, and 黃明華. "Algorithms for Parallel Association Rules Mining." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/88224762660139352543.
Full textAbstract:
碩士<br>國立臺灣科技大學<br>管理研究所資訊管理學程<br>87<br>Mining association rules is an important task. Many parallel algorithms have been proposed to expedite the execution of the mining process. In this thesis, we propose a parallel algorithm called ''PBSM'' for shared-disk environments, and implement the PBSM algorithm on an nCUBE parallel computer.
In the PBSM algorithm, mining process is divided into two steps. In the first step, multiple processors are used to generate frequent itemsets. Then, in the second phase, a chosen processor is used to generate the related association rules.
Through boolean-based table operations, the PBSM algorithm needs not generate candidate itemsets─which constitute the major part of execution time in the previous Apriori-based mining algorithms. Further-more, in the PBSM algorithm, each processor works independently in generating frequent itemsets. There is no need to send messages for itemsets, supports or counts between processors. As a result, our PBSM algorithm shows a superb performance compared to the existing parallel mining algorithms.
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Zhen, Hao, and 振昊. "DPARM: Differential Privacy Association Rules Mining." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/xqj7yw.
Full textAbstract:
碩士<br>國立臺灣大學<br>電機工程學研究所<br>107<br>In contemporary society, the rapid expansion of data volume has driven the development of data analysis techniques, which makes decision automation possible. Association analysis is an important task in data analysis. The goal is to find all co-occurrence relationships from the transactional dataset, i.e. frequent itemsets or confident association rules. An association rule consists of two parts, the antecedent and the consequent, which means that if the antecedent occurs then the consequent is also possible to happen. Confident association rules are those association rules with larger possibility, which can help people better discover patterns and develop corresponding strategies.
The process of data analysis can be highly summarized as a set of queries, where each query is a real-valued function of the dataset. However, without any restriction and protection, accessing the dataset to answer the queries may lead to the disclosure of individual privacy. Therefore, techniques for privacy-preserving data analysis has received increasing attention. People are eager to find a strong, mathematically rigorous, and socio-cognitive-conform definition of privacy. Differential privacy is such a privacy definition that manages and quantifies the privacy risks faced by individuals in data analysis through the parameter called the privacy level. In general, differential privacy can be achieved by adding delicate noise to the query results.
In this thesis, we focus on differential privacy association rules mining with multiple support thresholds, and solve the challenges existing in the state-of-art works. We propose and implement the DPARM algorithm, which uses multiple support thresholds to reduce the number of candidate itemsets while reflecting the real nature of the items, and uses random truncation and uniform partition to lower the dimensionality of the dataset. Both of these are helpful to reduce the sensitivity of the queries, thereby reducing the scale of the required noise and improving the utility of the mining results. We also stabilize the noise scale by adaptively allocating the privacy levels, and bound the overall privacy loss. In addition, we prove that the DPARM algorithm satisfies ex post differential privacy, and verify the utility of the DPARM algorithm through a series of experiments.
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Wu, Chieh-Ming, and 吳界明. "Data mining for generalized association rules and privacy preservingData mining for generalized association rules and privacy preserving." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/85543535661382633122.
Full textAbstract:
博士<br>國立雲林科技大學<br>工程科技研究所博士班<br>99<br>Data mining is an analysis method used to extract the unknown and latent information that hides in large dataset which has usable information. In the last few years the data mining model and method have long-term progress and the association rule mining is most often applied. The association rule research focus on discussion how to discover single level association rule effectiveness in the large dataset. In the recent years more and more researchers start to study the problem of multiple level association rules that was advantageous in the knowledge economy modernized society. In accordance to the enterprise, it must utilize nimbly the more deeply and more detailed association rules to assist the superintendent to complete policy-making in the short time. For reach the above objective, this study proposed an efficient data structure, Frequent Closed Enumerable Table (FCET), to speed the generalized association rules mining.
In the other aspect, as a result of enterprise globalization acceleration, many sensitive individual information collection, processing and application involve to the individual privacy protection law. In addition, databases managed by enterprises also largely grow up. The databases store many individual sensitive material and corporation secret information. If the database suffers non-suitable access, it leads the security problem. Moreover, it causes the company secret restricted data and the individual material to be disclosed. Once the problem is not careful processed, it would possibly reduce the competitiveness of enterprise.
This study proposes an effective data structure which considers the privacy preserving in the mining process. In addition, it carries on the complete discussion from data mining and privacy the preserving related question. A greedy algorithm which considers the hiding cost was proposed here. The algorithm includes the sanitized procedure and exposed procedure protection of mechanism. Not only privacy preserving for public content but also useful information extraction are guarantee to reach. Moreover, after the sanitized processing, it achieves privacy preserving and knowledge extracting balanced effectively.
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Wang, Wei-Tse, and 王威澤. "A native XML database association rules mining method and a database compression approach using association rules mining." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/47594741243843634353.
Full textAbstract:
碩士<br>朝陽科技大學<br>資訊管理系碩士班<br>91<br>With the advancement of technology and popularity of applications in enterprises’ information system, greater and greater amount of data is generated everyday. To properly store and access these data, database applications have come into play and become crucial. The main task of data mining is to help enterprises make their decisions by extracting useful information from the large amount of complicated data storage for reference, so this is why data mining has been recently paid more attention than ever. Also, more storage media for data is required for the increasing amount of data. For unlimited needs of increasing amount of data, it will be wise to provide an efficient data compression technique to reduce the cost.
The thesis proposes the related research on data mining. First of all, it is different from data mining fields based primarily on relational database. We propose a data mining method for native XML database. It can extract some knowledge from native XML database. Secondly, propose a semantic association rule – the rule that is extracted from data mining method. Convert it to the semantic association rule from the proposed procedures so as to make it more legible and easier to users as reference. Finally, propose a database compression using association rules mining. The method compresses the database for reducing the cost of storage. And from the association rules mining, it finds the association among these data. These association rules are further taken as reference for the organizations when making their strategic steps.
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SHAN, BO-YANG, and 單柏揚. "Using Sequential Pattern Mining to Enhance iMonsters Game Rules." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/95bqtn.
Full textAbstract:
碩士<br>亞洲大學<br>行動商務與多媒體應用學系<br>107<br>In the era of the rapid development of IoT, artificial intelligence and big data, Information security issues are on the rise and the importance of cyber security has gradually gained attention. Many studies using game-based learning to teach the cyber security concept and literacy have gained good results. For example, "iMonsters" card game, developed by KDELab of Asia University, currently has been effective in the teaching and has a lot of favorite players. But according to the feedback from the players, there still exist some problems. In this study, we will solve these problems to achieve a better balance between the gameplay, domain knowledge and education. We first proposed the game refining algorithm using Sequential Pattern Mining to strengthen the iMonsters game rules. For example, some anomalies have been found including the existence of the barrier of the game and the different interpretations of the game rules. Therefore, the game rule refining algorithm can help us to refine the game rules.
We then conducted the teaching and testing of the card game through the winter camp of the Asian University. According to the results of pre-tests, post-tests and questionnaires, we further modified the rules of the game and obtained the players' learning status. In addition, we applied the Internet security knowledge building algorithm proposed by the KDELab to analyze the collected real Internet security incidents and to modify the iMonsters card game rules if the new incidents cannot be solved. Finally the game rules evolution algorithm was proposed to modify the game rules to conform to the ever-changing cyber-attack techniques.
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Yang, Nai-Hua, and 楊乃樺. "Mining Multidimensional Association Rules for Market Segmentation." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/78f64c.
Full textAbstract:
碩士<br>銘傳大學<br>資訊管理學系碩士班<br>95<br>Today is a customer-oriented market. Enterprises need to give every customer
appropriate service. The more precise information can make accurate and profitable
strategies. Association rules provide correlations between data items in large numbers
of data. The further exploration is to discover relationship between customer’s
features and customer purchasing behaviors.
This paper proposes a new method to discover mining multidimensional
association rule for market segmentation. We use conditional databases to discover
multidimensional association rule, do not scan the target database many times and
combine cluster method to automatically discretize numerical-type attributes. Our
method analyzes CRM data from two different points of view. One is the product
combinations according to different customer features; another is the customer
features according to purchased products of customers. These two different points of
view can provide decision-makers to establish customer profiles, segment market and
make strategies more accurately.
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Jen-Feng, Li. "Mining Association Rules in Time-series Databases." 2005. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0001-2507200511203800.
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Ying-Hsiang, Wen. "Parallel Hardware Architecture for Mining Association Rules." 2006. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0001-2507200610192300.
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Wang, Hsing-Kai, and 王星凱. "An Efficient Distributed Association Rules Mining System." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/75735815815407828838.
Full textAbstract:
碩士<br>淡江大學<br>資訊管理學系<br>91<br>Association rule mining can help the enterprises to capture the consumer behaviors and develop effective marketing strategies. However, the size of transaction database is increasing everyday, how to get timely mining results becomes a serious problem. In this paper, we propose an Effective Distributed Association rule Mining System, EDAMS, to cope with this problem. Unlike other distributed mining systems, a dedicated node is used as data server to collect exchange data among nodes. Thus, the point-to-point broadcasts are avoided and therefore the number of message exchanged is greatly reduced from O(n2) to O(n). Besides, to reduce the total amount of message, the DHP algorithm[2] is used as the basis algorithm to reduce the number of candidate 2-itemsets. According to our experimental results, the EDAMS achieve steadily increasing speedup ration ranging from 100,000 to 700,000 transaction data. Also, the speedup ratio is superior to those in the previous work[7][9]. It clearly demonstrates the effectiveness of our system.
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Li, Jian-Ming, and 李建明. "Mining Quantitative Association Rules in Disease Databases." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/76433798386921713653.
Full textAbstract:
碩士<br>國立臺灣大學<br>電機工程學研究所<br>88<br>With the computerization of medical information and popularity of medical database, the amount of data grows much more rapidly than ever. There must be numerous known or unknown information hidden behind these data. Traditional statistical approach is not suit for processing such large amount of data. A technique called “Data Mining” is emerging in which the “Association Rules” is the one focusing on the relationship among data items.
The technique of mining association rules was first introduced to search the pattern of items that a customer may buy in a supermarket. It can also be extended for mining association rules from a relational database. There are two kinds of attributes in a relational database, one is quantitative and the other is categorical. In this thesis, we introduce a statistical method to finely partition the values of a quantitative attribute into a set of intervals. Different from the previous method which equally partitions the range of an attribute, we suggest a method based on the observation of the data distribution. And we use the mean and standard deviation of each attribute as two parameters of partition. This choice reflects the bias of databases so that it can improve the effectiveness of analysis in highly skewed data. To demonstrate the feasibility of our method, we combine two effective rule-mining algorithms called the DHP algorithm and the Boolean algorithm. With the combination, we can mine association rules from the relational database.
Finally, we use this approach on two disease databases. We show the experimental results and compare them with previous methods. The results reveal that our method generated less noises and it was executed easier.
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Wen, Ying-Hsiang, and 溫英翔. "Parallel Hardware Architecture for Mining Association Rules." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/84140141294459812790.
Full textAbstract:
碩士<br>國立臺灣大學<br>電機工程學研究所<br>94<br>Generally speaking, to implement Apriori-based association rule mining in hardware, one has to load candidate itemsets and a database into the hardware. Since the capacity of the hardware architecture is fixed, if the number of candidate itemsets or the number of items in the database is larger than the hardware capacity, the items are loaded into the hardware separately. The time complexity is in proportion to the number of candidate
itemsets multiplied by the number of items in the database. Too many candidate itemsets and a large database would create a performance bottleneck. In this thesis, we propose a HAsh-based and PiPelIned architecture (abbreviated as HAPPI) for hardware-enhanced association rule mining. We apply the pipeline methodology in the HAPPI architecture to compare itemsets with the database and collect useful information for reducing the number
of candidate itemsets and items in the database simultaneously. When the database is fed into the hardware, candidate itemsets are compared with the items in the database to find frequent itemsets. At the same time, trimming information is collected from each
transaction. In addition, itemsets are generated from transactions and hashed into a hash table. The useful trimming information and the hash table enable us to reduce the number of items in the database and the number of candidate itemsets. Therefore, we can effectively reduce the frequency of loading the database into the hardware. As such, HAPPI solves the bottleneck problem in Apriori-based hardware schemes. We also derive some properties to investigate the performance of this hardware implementation. As shown by the experiment results, HAPPI significantly outperforms the previous hardware approach in terms of execution cycles.
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Tung, Chien-Hung, and 董建弘. "Incremental XML Association Rules Mining MiningUsing XQuery." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/55107331051607385174.
Full textAbstract:
碩士<br>國立高雄第一科技大學<br>資訊管理所<br>94<br>ABSTRACT
XML has already been recognized as a standard for electronic data interchange
over the Internet. We believe a large amount of data will be represented
and stored in XML format in the near future. Therefore, we think it
will be indispensable to develop tools for mining information directly from
XML data. Although many well-known data minig methods have been developed,
they are almost based on relational database formats. In this paper, we
will propose an algorithm, called IXARM (Incremental XML Association
Rules Mining), which does not only extract association rules from XML
documents, but also offer flexible incremental mining tasks. That is, even
when there are many INSERT, DELETE or UPDATE events performed on
source XML documents, our algorithm re-computes the modified part only
and then combines the previous result to build new association rules in an efficient
way. We also have conducted experiment to show the accuracy and
performances are all satisfactory for most of the assocation rule mining applications
on XML documents.
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Wan-chuen, Lin, and 林琬純. "Mining Association Rules with Multi-dimensional Constraints." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/65164683786356321966.
Full textAbstract:
碩士<br>國立臺灣大學<br>資訊管理研究所<br>91<br>Association rule mining is an important issue in the area of data mining. Frequent itemset discovery is the key factor in performance of association rule mining. Frequent itemset mining algorithms often generate a very large number of frequent itemsets and rules, which reduce not only the efficiency but also the effectiveness of the mining algorithms since only the subset of the complete frequent itemsets and association rules is of interest to users, and users need additional post-processing to filter through a large number of mined rules to find the useful ones. Constraint-based mining enables users to concentrate on mining itemsets that are interesting to themselves, which improves the efficiency of mining tasks.
Previously proposed methods consider that items in transactions are characterized only by single attribute value. In the real world, users may want to keep records of items with respect to more than one attribute and impose constraints on multiple dimensional attributes. In this thesis, we enhance the item representation by associating items with a number of attributes, so-called multi-dimensional items. We have defined and characterized some multi-dimensional constraints. Moreover, we have discussed the properties of those constraints and developed algorithms E-CFG and its generic form, GE-CFG, for mining frequent itemsets with multi-dimensional constraints. The experimental results show that both E-CFG and GE-CFG algorithms outperform the FP-growth+ algorithm for all the cases.
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Han, Hui Ching, and 韓惠靜. "Mining Association Rules Among Time-series Databases." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/66580094049643941505.
Full textAbstract:
碩士<br>國立臺灣大學<br>資訊管理研究所<br>91<br>While more and more data generated in the form of time-series, there are much more needs to find frequent patterns in time-series data. Time-series data mining becomes more and more popular in recent research areas and has broad applications like analysis of customer purchase patterns, web traversal patterns, etc.
Let’s consider the example of stock price fluctuation and trading volume fluctuation patterns of TWSE. There may be some implications that when the stock price went upwards two days before, the stock trading volume may go upwards in following days. The price and trading volume of some leading companies may also affect those changes of other companies in the same industry. It’s interesting for us to find the relationships between stock fluctuation patterns. If we could find out some association rules between stock fluctuation patterns, we can predicate more precisely the trends of stock markets.
In this thesis, we propose an algorithm to mine the association rules among time-series data. We view the transaction data describing different attributes or subjects as lines, and then we find association rules among those lines. We’ll introduce a method to find the frequent lines efficiently by constructing the bitmaps of frequent patterns, the method is helpful to reduce the number of database scans. The experimental results show that our proposed algorithm outperforms the Apriori-like approach by several orders of magnitude.
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Hsi, Lo Yuan, and 羅元禧. "The Association Rules used on Web Mining." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/20237348252767485110.
Full textAbstract:
碩士<br>國立臺北大學<br>企業管理學系<br>91<br>With the trend of increasing web population and data, there is existing a common problem that the pages provided on web are not distinguished with others. All of context provided, web structure and navigation path on web are regulated by the designator. The designator had set up the web only in his opinion, most of them can’t make sure if the context on web could fit user’s real demand.
This research would discuss how to analyze the data with Data Mining Method. We had dealt with these data in the first step, and then we would find out the relationship between the data, for instance, association rule. Association rule is one kind of the Data Mining Methods, its primary objective is to find out the association between some specific data items. Because it’s widely accepted by the public, it had been applied to several fields. Using association rule to analyze the registered data makes the result is much fit to general web’s users.
Comparing with these three kinds of association rule methods application, the former two methods are applied to web mining in order to find out web association. But the limitation of research method had made those methods application impossible. The second method is to make a solution with long linear method, but it still exists some problems that the conclusions are not in unanimity. The latest method is solve this problem with applying Markov Chain Monte Carlo method to web mining and could include the association between 11 pages in one time.
We had used the daily records of NTPU Business Administration Department web to support our research. With the three kinds methods to analyze many navigating information of web users to provide customer-orientation service and information needed. We could sort out these webs base on our conclusion and that would be helpful to set up webs.
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郭瑞男. "Mining Quantitative Association Rules with Density Constraint." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/13322880493417591245.
Full textAbstract:
碩士<br>國立臺灣師範大學<br>資訊教育研究所<br>91<br>A new approach, called PQAR (Partition-based Quantitative Association Rules mining) algorithm, is proposed in this thesis for mining quantitative association rules. This approach finds out all the frequent interval itemsets that satisfy the minimum relative density requirement based on space partitioning method, and the quantitative association rules are produced from these interval itemsets. When mining frequent interval itemsets, PQAR algorithm considers not only the minimum support as the filtering condition, but also the minimum relative density to prevent finding the intervals in which data distribution is sparse. In addition, based on space partitioning method to find out the largest intervals that meet the threshold requirements, the number of qualified intervals is reduced such that the resulting rules are significant and concise. Furthermore, because the number of times to scan database is reduced possibly in PQAR algorithm, the mining time is shorten considerably than the previous approaches. The experimental results show that, when testing data sets with various supports and relative densities setting, PQAR algorithm obtains results with high accuracy and recall in most cases. Moreover, under the same accuracy condition, PQAR algorithm takes much less time than QAR algorithm.