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Sooriyaarachchi, Wickramaratna Kasun Jayamal. "DS-ARM: An Association Rule Based Predictor that Can Learn from Imperfect Data." Scholarly Repository, 2010. http://scholarlyrepository.miami.edu/oa_dissertations/159.
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Over the past decades, many industries have heavily spent on computerizing their work environments with the intention to simplify and expedite access to information and its processing. Typical of real-world data are various types of imperfections, uncertainties, ambiguities, that have complicated attempts at automated knowledge discovery. Indeed, it soon became obvious that adequate methods to deal with these problems were critically needed. Simple methods such as "interpolating" or just ignoring data imperfections being found often to lead to inferences of dubious practical value, the search for appropriate modification of knowledge-induction techniques began. Sometimes, rather non-standard approaches turned out to be necessary. For instance, the probabilistic approaches by earlier works are not sufficiently capable of handling the wider range of data imperfections that appear in many new applications (e.g., medical data). Dempster-Shafer theory provides a much stronger framework, and this is why it has been chosen as the fundamental paradigm exploited in this dissertation. The task of association rule mining is to detect frequently co-occurring groups of items in transactional databases. The majority of the papers in this field concentrate on how to expedite the search. Less attention has been devoted to how to employ the identified frequent itemsets for prediction purposes; worse still, methods to tailor association-mining techniques so that they can handle data imperfections are virtually nonexistent. This dissertation proposes a technique referred to by the acronym DS-ARM (Dempster-Shafer based Association Rule Mining) where the DS-theoretic framework is used to enhance a more traditional association-mining mechanism. Of particular interest is here a method to employ the knowledge of partial contents of a "shopping cart" for the prediction of what else the customer is likely to add to it. This formalized problem has many applications in the analysis of medical databases. A recently-proposed data structure, an itemset tree (IT-tree), is used to extract association rules in a computationally efficient manner, thus addressing the scalability problem that has disqualified more traditional techniques from real-world applications. The proposed algorithm is based on the Dempster-Shafer theory of evidence combination. Extensive experiments explore the algorithm's behavior; some of them use synthetically generated data, others relied on data obtained from a machine-learning repository, yet others use a movie ratings dataset or a HIV/AIDS patient dataset.
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Samuel, Jarvie John. "Elicitation of Protein-Protein Interactions from Biomedical Literature Using Association Rule Discovery." Thesis, University of North Texas, 2010. https://digital.library.unt.edu/ark:/67531/metadc30508/.
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Extracting information from a stack of data is a tedious task and the scenario is no different in proteomics. Volumes of research papers are published about study of various proteins in several species, their interactions with other proteins and identification of protein(s) as possible biomarker in causing diseases. It is a challenging task for biologists to keep track of these developments manually by reading through the literatures. Several tools have been developed by computer linguists to assist identification, extraction and hypotheses generation of proteins and protein-protein interactions from biomedical publications and protein databases. However, they are confronted with the challenges of term variation, term ambiguity, access only to abstracts and inconsistencies in time-consuming manual curation of protein and protein-protein interaction repositories. This work attempts to attenuate the challenges by extracting protein-protein interactions in humans and elicit possible interactions using associative rule mining on full text, abstracts and captions from figures available from publicly available biomedical literature databases. Two such databases are used in our study: Directory of Open Access Journals (DOAJ) and PubMed Central (PMC). A corpus is built using articles based on search terms. A dataset of more than 38,000 protein-protein interactions from the Human Protein Reference Database (HPRD) is cross-referenced to validate discovered interactive pairs. A set of an optimal size of possible binary protein-protein interactions is generated to be made available for clinician or biological validation. A significant change in the number of new associations was found by altering the thresholds for support and confidence metrics. This study narrows down the limitations for biologists in keeping pace with discovery of protein-protein interactions via manually reading the literature and their needs to validate each and every possible interaction.
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Leshi, Olumide. "An Approach to Extending Ontologies in the Nanomaterials Domain." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-170255.
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As recently as the last decade or two, data-driven science workflows have become increasingly popular and semantic technology has been relied on to help align often parallel research efforts in the different domains and foster interoperability and data sharing. However, a key challenge is the size of the data and the pace at which it is being generated, so much that manual procedures lag behind. Thus, eliciting automation of most workflows. In this study, the effort is to continue investigating ways by which some tasks performed by experts in the nanotechnology domain, specifically in ontology engineering, could benefit from automation. An approach, featuring phrase-based topic modelling and formal topical concept analysis is further motivated, together with formal implication rules, to uncover new concepts and axioms relevant to two nanotechnology-related ontologies. A corpus of 2,715 nanotechnology research articles helps showcase that the approach can scale, as seen in a number of experiments conducted. The usefulness of document text ranking as an alternative form of input to topic models is highlighted as well as the benefit of implication rules to the task of concept discovery. In all, a total of 203 new concepts are uncovered by the approach to extend the referenced ontologies
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Wong, Wai-kit. "Security in association rule mining." Click to view the E-thesis via HKUTO, 2007. http://sunzi.lib.hku.hk/HKUTO/record/B39558903.
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Wong, Wai-kit, and 王偉傑. "Security in association rule mining." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B39558903.
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Palanisamy, Senthil Kumar. "Association rule based classification." Link to electronic thesis, 2006. http://www.wpi.edu/Pubs/ETD/Available/etd-050306-131517/.
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Thesis (M.S.)--Worcester Polytechnic Institute.<br>Keywords: Itemset Pruning, Association Rules, Adaptive Minimal Support, Associative Classification, Classification. Includes bibliographical references (p.70-74).
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Zhang, Ya Klein Cerry M. "Association rule mining in cooperative research." Diss., Columbia, Mo. : University of Missouri--Columbia, 2009. http://hdl.handle.net/10355/6540.
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The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Title from PDF of title page (University of Missouri--Columbia, viewed January 26, 2010). Thesis advisor: Dr. Cerry M. Klein. Includes bibliographical references.
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Icev, Aleksandar. "DARM distance-based association rule mining." Link to electronic thesis, 2003. http://www.wpi.edu/Pubs/ETD/Available/etd-0506103-132405.
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Vithal, Kadam Omkar. "Novel applications of Association Rule Mining- Data Stream Mining." AUT University, 2009. http://hdl.handle.net/10292/826.
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From the advent of association rule mining, it has become one of the most researched areas of data exploration schemes. In recent years, implementing association rule mining methods in extracting rules from a continuous flow of voluminous data, known as Data Stream has generated immense interest due to its emerging applications such as network-traffic analysis, sensor-network data analysis. For such typical kinds of application domains, the facility to process such enormous amount of stream data in a single pass is critical.
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Lin, Weiyang. "Association rule mining for collaborative recommender systems." Link to electronic version, 2000. http://www.wpi.edu/Pubs/ETD/Available/etd-0515100-145926.
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Rantzau, Ralf. "Extended concepts for association rule discovery." [S.l. : s.n.], 1997. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB8937694.
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Ahmed, Shakil. "Strategies for partitioning data in association rule mining." Thesis, University of Liverpool, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.415661.
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Bogorny, Vania. "Enhancing spatial association rule mining in geographic databases." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2006. http://hdl.handle.net/10183/7841.
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A técnica de mineração de regras de associação surgiu com o objetivo de encontrar conhecimento novo, útil e previamente desconhecido em bancos de dados transacionais, e uma grande quantidade de algoritmos de mineração de regras de associação tem sido proposta na última década. O maior e mais bem conhecido problema destes algoritmos é a geração de grandes quantidades de conjuntos freqüentes e regras de associação. Em bancos de dados geográficos o problema de mineração de regras de associação espacial aumenta significativamente. Além da grande quantidade de regras e padrões gerados a maioria são associações do domínio geográfico, e são bem conhecidas, normalmente explicitamente representadas no esquema do banco de dados. A maioria dos algoritmos de mineração de regras de associação não garantem a eliminação de dependências geográficas conhecidas a priori. O resultado é que as mesmas associações representadas nos esquemas do banco de dados são extraídas pelos algoritmos de mineração de regras de associação e apresentadas ao usuário. O problema de mineração de regras de associação espacial pode ser dividido em três etapas principais: extração dos relacionamentos espaciais, geração dos conjuntos freqüentes e geração das regras de associação. A primeira etapa é a mais custosa tanto em tempo de processamento quanto pelo esforço requerido do usuário. A segunda e terceira etapas têm sido consideradas o maior problema na mineração de regras de associação em bancos de dados transacionais e tem sido abordadas como dois problemas diferentes: “frequent pattern mining” e “association rule mining”. Dependências geográficas bem conhecidas aparecem nas três etapas do processo. Tendo como objetivo a eliminação dessas dependências na mineração de regras de associação espacial essa tese apresenta um framework com três novos métodos para mineração de regras de associação utilizando restrições semânticas como conhecimento a priori. O primeiro método reduz os dados de entrada do algoritmo, e dependências geográficas são eliminadas parcialmente sem que haja perda de informação. O segundo método elimina combinações de pares de objetos geográficos com dependências durante a geração dos conjuntos freqüentes. O terceiro método é uma nova abordagem para gerar conjuntos freqüentes não redundantes e sem dependências, gerando conjuntos freqüentes máximos. Esse método reduz consideravelmente o número final de conjuntos freqüentes, e como conseqüência, reduz o número de regras de associação espacial.<br>The association rule mining technique emerged with the objective to find novel, useful, and previously unknown associations from transactional databases, and a large amount of association rule mining algorithms have been proposed in the last decade. Their main drawback, which is a well known problem, is the generation of large amounts of frequent patterns and association rules. In geographic databases the problem of mining spatial association rules increases significantly. Besides the large amount of generated patterns and rules, many patterns are well known geographic domain associations, normally explicitly represented in geographic database schemas. The majority of existing algorithms do not warrant the elimination of all well known geographic dependences. The result is that the same associations represented in geographic database schemas are extracted by spatial association rule mining algorithms and presented to the user. The problem of mining spatial association rules from geographic databases requires at least three main steps: compute spatial relationships, generate frequent patterns, and extract association rules. The first step is the most effort demanding and time consuming task in the rule mining process, but has received little attention in the literature. The second and third steps have been considered the main problem in transactional association rule mining and have been addressed as two different problems: frequent pattern mining and association rule mining. Well known geographic dependences which generate well known patterns may appear in the three main steps of the spatial association rule mining process. Aiming to eliminate well known dependences and generate more interesting patterns, this thesis presents a framework with three main methods for mining frequent geographic patterns using knowledge constraints. Semantic knowledge is used to avoid the generation of patterns that are previously known as non-interesting. The first method reduces the input problem, and all well known dependences that can be eliminated without loosing information are removed in data preprocessing. The second method eliminates combinations of pairs of geographic objects with dependences, during the frequent set generation. A third method presents a new approach to generate non-redundant frequent sets, the maximal generalized frequent sets without dependences. This method reduces the number of frequent patterns very significantly, and by consequence, the number of association rules.
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Shrestha, Anuj. "Association Rule Mining of Biological Field Data Sets." Thesis, North Dakota State University, 2017. https://hdl.handle.net/10365/28394.
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Association rule mining is an important data mining technique, yet, its use in association analysis of biological data sets has been limited. This mining technique was applied on two biological data sets, a genome and a damselfly data set. The raw data sets were pre-processed, and then association analysis was performed with various configurations. The pre-processing task involves minimizing the number of association attributes in genome data and creating the association attributes in damselfly data. The configurations include generation of single/maximal rules and handling single/multiple tier attributes. Both data sets have a binary class label and using association analysis, attributes of importance to each of these class labels are found. The results (rules) from association analysis are then visualized using graph networks by incorporating the association attributes like support and confidence, differential color schemes and features from the pre-processed data.<br>Bioinformatics Seed Grant Program NIH/UND<br>National Science Foundation (NSF) Grant IIA-1355466
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Chudán, David. "Association rule mining as a support for OLAP." Doctoral thesis, Vysoká škola ekonomická v Praze, 2010. http://www.nusl.cz/ntk/nusl-201130.
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The aim of this work is to identify the possibilities of the complementary usage of two analytical methods of data analysis, OLAP analysis and data mining represented by GUHA association rule mining. The usage of these two methods in the context of proposed scenarios on one dataset presumes a synergistic effect, surpassing the knowledge acquired by these two methods independently. This is the main contribution of the work. Another contribution is the original use of GUHA association rules where the mining is performed on aggregated data. In their abilities, GUHA association rules outperform classic association rules referred to the literature. The experiments on real data demonstrate the finding of unusual trends in data that would be very difficult to acquire using standard methods of OLAP analysis, the time consuming manual browsing of an OLAP cube. On the other hand, the actual use of association rules loses a general overview of data. It is possible to declare that these two methods complement each other very well. The part of the solution is also usage of LMCL scripting language that automates selected parts of the data mining process. The proposed recommender system would shield the user from association rules, thereby enabling common analysts ignorant of the association rules to use their possibilities. The thesis combines quantitative and qualitative research. Quantitative research is represented by experiments on a real dataset, proposal of a recommender system and implementation of the selected parts of the association rules mining process by LISp-Miner Control Language. Qualitative research is represented by structured interviews with selected experts from the fields of data mining and business intelligence who confirm the meaningfulness of the proposed methods.
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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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Baez, Monroy Vicente Oswaldo. "Neural networks as artificial memories for association rule mining." Thesis, University of York, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.437620.
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Fjällström, Peter. "A way to compare measures in association rule mining." Thesis, Umeå universitet, Statistik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-124903.
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Mahmood, Qazafi. "LC - an effective classification based association rule mining algorithm." Thesis, University of Huddersfield, 2014. http://eprints.hud.ac.uk/id/eprint/24274/.
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Classification using association rules is a research field in data mining that primarily uses association rule discovery techniques in classification benchmarks. It has been confirmed by many research studies in the literature that classification using association tends to generate more predictive classification systems than traditional classification data mining techniques like probabilistic, statistical and decision tree. In this thesis, we introduce a novel data mining algorithm based on classification using association called “Looking at the Class” (LC), which can be used in for mining a range of classification data sets. Unlike known algorithms in classification using the association approach such as Classification based on Association rule (CBA) system and Classification based on Predictive Association (CPAR) system, which merge disjoint items in the rule learning step without anticipating the class label similarity, the proposed algorithm merges only items with identical class labels. This saves too many unnecessary items combining during the rule learning step, and consequently results in large saving in computational time and memory. Furthermore, the LC algorithm uses a novel prediction procedure that employs multiple rules to make the prediction decision instead of a single rule. The proposed algorithm has been evaluated thoroughly on real world security data sets collected using an automated tool developed at Huddersfield University. The security application which we have considered in this thesis is about categorizing websites based on their features to legitimate or fake which is a typical binary classification problem. Also, experimental results on a number of UCI data sets have been conducted and the measures used for evaluation is the classification accuracy, memory usage, and others. The results show that LC algorithm outperformed traditional classification algorithms such as C4.5, PART and Naïve Bayes as well as known classification based association algorithms like CBA with respect to classification accuracy, memory usage, and execution time on most data sets we consider.
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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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Weitl, Harms Sherri K. "Temporal association rule methodologies for geo-spatial decision support /." free to MU campus, to others for purchase, 2002. http://wwwlib.umi.com/cr/mo/fullcit?p3091989.
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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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Hahsler, Michael, Kurt Hornik, and Thomas Reutterer. "Implications of probabilistic data modeling for rule mining." Institut für Statistik und Mathematik, WU Vienna University of Economics and Business, 2005. http://epub.wu.ac.at/764/1/document.pdf.
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Mining association rules is an important technique for discovering meaningful patterns in transaction databases. In the current literature, the properties of algorithms to mine associations are discussed in great detail. In this paper we investigate properties of transaction data sets from a probabilistic point of view. We present a simple probabilistic framework for transaction data and its implementation using the R statistical computing environment. The framework can be used to simulate transaction data when no associations are present. We use such data to explore the ability to filter noise of confidence and lift, two popular interest measures used for rule mining. Based on the framework we develop the measure hyperlift and we compare this new measure to lift using simulated data and a real-world grocery database.<br>Series: Research Report Series / Department of Statistics and Mathematics
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Marinica, Claudia. "Association Rule Interactive Post-processing using Rule Schemas and Ontologies - ARIPSO." Phd thesis, Université de Nantes, 2010. http://tel.archives-ouvertes.fr/tel-00912580.
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This thesis is concerned with the merging of two active research domains: Knowledge Discovery in Databases (KDD), more precisely the Association Rule Mining technique, and Knowledge Engineering (KE) with a main interest in knowledge representation languages developed around the Semantic Web. In Data Mining, the usefulness of association rule technique is strongly limited by the huge amount and the low quality of delivered rules. Experiments show that rules become almost impossible to use when their number exceeds 100. At the same time, nuggets are often represented by those rare (low support) unexpected association rules which are surprising to the user. Unfortunately, the lower the support is, the larger the volume of rules becomes. Thus, it is crucial to help the decision maker with an efficient technique to reduce the number of rules. To overcome this drawback, several methods have been proposed in the literature such as itemset concise representations, redundancy reduction, filtering, ranking and post-processing. Even though rule interestingness strongly depends on user knowledge and goals, most of the existing methods are generally based on data structure. For instance, if the user looks for unexpected rules, all the already known rules should be pruned. Or, if the user wants to focus on specific family of rules, only this subset of rules should be selected. In this context, we address two main issues: the integration of user knowledge in the discovery process and the interactivity with the user. The first issue requires defining an adapted formalism to express user knowledge with accuracy and flexibility such as ontologies in the Semantic Web. Second, the interactivity with the user allows a more iterative mining process where the user can successively test different hypotheses or preferences and focus on interesting rules. The main contributions of this work can be summarized as follows: (i) A model to represent user knowledge. First, we propose a new rule-like formalism, called Rule Schema, which allows the user to define his/her expectations regarding the rules through ontology concepts. Second, ontologies allow the user to express his/her domain knowledge by means of a high semantic model. Last, the user can choose among a set of Operators for interactive processing the one to be applied over each Rule Schema (i.e. pruning, conforming, unexpectedness, . . . ). (ii) A new post-processing approach, called ARIPSO (Association Rule Interactive Post-processing using rule Schemas and Ontologies), which helps the user to reduce the volume of the discovered rules and to improve their quality. It consists in an interactive process integrating user knowledge and expectations by means of the proposed model. At each step of ARIPSO, the interactive loop allows the user to change the provided information and to reiterate the post-processing phase which produces new results. (iii) The implementation in post-processing of the proposed approach. The developed tool is complete and operational, and it implements all the functionalities described in the approach. Also, it makes the connection between different elements like the set of rules and rule schemas stored in PMML/XML files, and the ontologies stored in OWL files and inferred by the Pellet reasoner. (iv) An adapted implementation without post-processing, called ARLIUS (Association Rule Local mining Interactive Using rule Schemas), consisting in an interactive local mining process guided by the user. It allows the user to focus on interesting rules without the necessity to extract all of them, and without minimum support limit. In this way, the user may explore the rule space incrementally, a small amount at each step, starting from his/her own expectations and discovering their related rules. (v) The experimental study analyzing the approach efficiency and the discovered rule quality. For this purpose, we used a real-life and large questionnaire database concerning customer satisfaction. For ARIPSO, the experimentation was carried out in complete cooperation with the domain expert. For different scenarios, from an input set of nearly 400 thousand association rules, ARIPSO filtered between 3 and 200 rules validated by the expert. Clearly, ARIPSO allows the user to significantly and efficiently reduce the input rule set. For ARLIUS, we experimented different scenarios over the same questionnaire database and we obtained reduced sets of rules (less than 100) with very low support.
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Rahman, Sardar Muhammad Monzurur, and mrahman99@yahoo com. "Data Mining Using Neural Networks." RMIT University. Electrical & Computer Engineering, 2006. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080813.094814.
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Data mining is about the search for relationships and global patterns in large databases that are increasing in size. Data mining is beneficial for anyone who has a huge amount of data, for example, customer and business data, transaction, marketing, financial, manufacturing and web data etc. The results of data mining are also referred to as knowledge in the form of rules, regularities and constraints. Rule mining is one of the popular data mining methods since rules provide concise statements of potentially important information that is easily understood by end users and also actionable patterns. At present rule mining has received a good deal of attention and enthusiasm from data mining researchers since rule mining is capable of solving many data mining problems such as classification, association, customer profiling, summarization, segmentation and many others. This thesis makes several contributions by proposing rule mining methods using genetic algorithms and neural networks. The thesis first proposes rule mining methods using a genetic algorithm. These methods are based on an integrated framework but capable of mining three major classes of rules. Moreover, the rule mining processes in these methods are controlled by tuning of two data mining measures such as support and confidence. The thesis shows how to build data mining predictive models using the resultant rules of the proposed methods. Another key contribution of the thesis is the proposal of rule mining methods using supervised neural networks. The thesis mathematically analyses the Widrow-Hoff learning algorithm of a single-layered neural network, which results in a foundation for rule mining algorithms using single-layered neural networks. Three rule mining algorithms using single-layered neural networks are proposed for the three major classes of rules on the basis of the proposed theorems. The thesis also looks at the problem of rule mining where user guidance is absent. The thesis proposes a guided rule mining system to overcome this problem. The thesis extends this work further by comparing the performance of the algorithm used in the proposed guided rule mining system with Apriori data mining algorithm. Finally, the thesis studies the Kohonen self-organization map as an unsupervised neural network for rule mining algorithms. Two approaches are adopted based on the way of self-organization maps applied in rule mining models. In the first approach, self-organization map is used for clustering, which provides class information to the rule mining process. In the second approach, automated rule mining takes the place of trained neurons as it grows in a hierarchical structure.
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Koukal, Bohuslav. "OLAP Recommender: Supporting Navigation in Data Cubes Using Association Rule Mining." Master's thesis, Vysoká škola ekonomická v Praze, 2017. http://www.nusl.cz/ntk/nusl-359132.
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Manual data exploration in data cubes and searching for potentially interesting and useful information starts to be time-consuming and ineffective from certain volume of the data. In my thesis, I designed, implemented and tested a system, automating the data cube exploration and offering potentially interesting views on OLAP data to the end user. The system is based on integration of two data analytics methods - OLAP analysis data visualisation and data mining, represented by GUHA association rules mining. Another contribution of my work is a research of possibilities how to solve differences between OLAP analysis and association rule mining. Implemented solutions of the differences include data discretization, dimensions commensurability, design of automatic data mining task algorithm based on the data structure and mapping definition between mined association rules and corresponding OLAP visualisation. The system was tested with real retail sales data and with EU structural funds data. The experiments proved that complementary usage of the association rule mining together with OLAP analysis identifies relationships in the data with higher success rate than the isolated use of both techniques.
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Yang, Wanzhong. "Granule-based knowledge representation for intra and inter transaction association mining." Queensland University of Technology, 2009. http://eprints.qut.edu.au/30398/.
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Abstract
With the phenomenal growth of electronic data and information, there are many demands for the development of efficient and effective systems (tools) to perform the issue of data mining tasks on multidimensional databases. Association rules describe associations between items in the same transactions (intra) or in different transactions (inter). Association mining attempts to find interesting or useful association rules in databases: this is the crucial issue for the application of data mining in the real world. Association mining can be used in many application areas, such as the discovery of associations between customers’ locations and shopping behaviours in market basket analysis.
Association mining includes two phases. The first phase, called pattern mining, is the discovery of frequent patterns. The second phase, called rule generation, is the discovery of interesting and useful association rules in the discovered patterns. The first phase, however, often takes a long time to find all frequent patterns; these also include much noise. The second phase is also a time consuming activity that can generate many redundant rules.
To improve the quality of association mining in databases, this thesis provides an alternative technique, granule-based association mining, for knowledge discovery in databases, where a granule refers to a predicate that describes common features of a group of transactions. The new technique first transfers transaction databases into basic decision tables, then uses multi-tier structures to integrate pattern mining and rule generation in one phase for both intra and inter transaction association rule mining. To evaluate the proposed new technique, this research defines the concept of meaningless rules by considering the co-relations between data-dimensions for intratransaction-association rule mining. It also uses precision to evaluate the effectiveness of intertransaction association rules. The experimental results show that the proposed technique is promising.
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Isik, Narin. "Fuzzy Spatial Data Cube Construction And Its Use In Association Rule Mining." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12606056/index.pdf.
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The popularity of spatial databases increases since the amount of the spatial data that need to be handled has increased by the use of digital maps, images from satellites, video cameras, medical equipment, sensor networks, etc. Spatial data are difficult to examine and extract interesting knowledge<br>hence, applications that assist decision-making about spatial data like weather forecasting, traffic supervision, mobile communication, etc. have been introduced. In this thesis, more natural and precise knowledge from spatial data is generated by construction of fuzzy spatial data cube and extraction of fuzzy association rules from it in order to improve decision-making about spatial data. This involves an extensive research about spatial knowledge discovery and how fuzzy logic can be used to develop it. It is stated that incorporating fuzzy logic to spatial data cube construction necessitates a new method for aggregation of fuzzy spatial data. We illustrate how this method also enhances the meaning of fuzzy spatial generalization rules and fuzzy association rules with a case-study about weather pattern searching. This study contributes to spatial knowledge discovery by generating more understandable and interesting knowledge from spatial data by extending spatial generalization with fuzzy memberships, extending the spatial aggregation in spatial data cube construction by utilizing weighted measures, and generating fuzzy association rules from the constructed fuzzy spatial data cube.
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Aljandal, Waleed A. "Itemset size-sensitive interestingness measures for association rule mining and link prediction." Diss., Manhattan, Kan. : Kansas State University, 2009. http://hdl.handle.net/2097/1119.
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Smith, David T. "A Formal Concept Analysis Approach to Association Rule Mining: The QuICL Algorithms." NSUWorks, 2009. http://nsuworks.nova.edu/gscis_etd/309.
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Association rule mining (ARM) is the task of identifying meaningful implication rules exhibited in a data set. Most research has focused on extracting frequent item (FI) sets and thus fallen short of the overall ARM objective. The FI miners fail to identify the upper covers that are needed to generate a set of association rules whose size can be exploited by an end user. An alternative to FI mining can be found in formal concept analysis (FCA), a branch of applied mathematics. FCA derives a concept lattice whose concepts identify closed FI sets and connections identify the upper covers. However, most FCA algorithms construct a complete lattice and therefore include item sets that are not frequent. An iceberg lattice, on the other hand, is a concept lattice whose concepts contain only FI sets. Only three algorithms to construct an iceberg lattice were found in literature. Given that an iceberg concept lattice provides an analysis tool to succinctly identify association rules, this study investigated additional algorithms to construct an iceberg concept lattice. This report presents the development and analysis of the Quick Iceberg Concept Lattice (QuICL) algorithms. These algorithms provide incremental construction of an iceberg lattice. QuICL uses recursion instead of iteration to navigate the lattice and establish connections, thereby eliminating costly processing incurred by past algorithms. The QuICL algorithms were evaluated against leading FI miners and FCA construction algorithms using benchmarks cited in literature. Results demonstrate that QuICL provides performance on the order of FI miners yet additionally derive the upper covers. QuICL, when combined with known algorithms to extract a basis of association rules from a lattice, offer a "best known" ARM solution. Beyond this, the QuICL algorithms have proved to be very efficient, providing an order of magnitude gains over other incremental lattice construction algorithms. For example, on the Mushroom data set, QuICL completes in less than 3 seconds. Past algorithms exceed 200 seconds. On T10I4D100k, QuICL completes in less than 120 seconds. Past algorithms approach 10,000 seconds. QuICL is proved to be the "best known" all around incremental lattice construction algorithm. Runtime complexity is shown to be O(l d i) where l is the cardinality of the lattice, d is the average degree of the lattice, and i is a mean function on the frequent item extents.
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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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Toprak, Serkan. "Data Mining For Rule Discovery In Relational Databases." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/12605356/index.pdf.
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Data is mostly stored in relational databases today. However, most data mining algorithms are not capable of working on data stored in relational databases directly. Instead they require a preprocessing step for transforming relational data into algorithm specified form. Moreover, several data mining algorithms provide solutions for single relations only. Therefore, valuable hidden knowledge involving multiple relations remains undiscovered. In this thesis, an implementation is developed for discovering multi-relational association rules in relational databases. The implementation is based on a framework providing a representation of patterns in relational databases, refinement methods of patterns, and primitives for obtaining necessary record counts from database to calculate measures for patterns. The framework exploits meta-data of relational databases for pruning search space of patterns. The implementation extends the
framework by employing Apriori algorithm for further pruning the search space and discovering relational recursive patterns. Apriori algorithm is used for finding large itemsets of tables, which are used to refine patterns. Apriori algorithm is modified by changing support calculation method for itemsets. A method
for determining recursive relations is described and a solution is
provided for handling recursive patterns using aliases. Additionally, continuous attributes of tables are discretized utilizing equal-depth partitioning. The implementation is
tested with gene localization prediction task of KDD Cup 2001 and
results are compared to those of the winner approach.
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Mahamaneerat, Wannapa Kay Shyu Chi-Ren. "Domain-concept mining an efficient on-demand data mining approach /." Diss., Columbia, Mo. : University of Missouri--Columbia, 2008. http://hdl.handle.net/10355/7195.
Full textAbstract:
Title from PDF of title page (University of Missouri--Columbia, viewed on February 24, 2010). The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Dissertation advisor: Dr. Chi-Ren Shyu. Vita. Includes bibliographical references.
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Schweickart, Ian R. W. "Investigating Post-Earnings-Announcement Drift Using Principal Component Analysis and Association Rule Mining." Scholarship @ Claremont, 2017. https://scholarship.claremont.edu/hmc_theses/94.
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Post-Earnings-Announcement Drift (PEAD) is commonly accepted in the fields of accounting and finance as evidence for stock market inefficiency. Less accepted are the numerous explanations for this anomaly. This project aims to investigate the cause for PEAD by harnessing the power of machine learning algorithms such as Principle Component Analysis (PCA) and a rule-based learning technique, applied to large stock market data sets. Based on the notion that the market is consumer driven, repeated occurrences of irrational behavior exhibited by traders in response to news events such as earnings reports are uncovered. The project produces findings in support of the PEAD anomaly using non-accounting nor financial methods. In particular, this project finds evidence for delayed price response exhibited in trader behavior, a common manifestation of the PEAD phenomenon.
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Kao, Hung-An. "Quality Prediction Modeling for Multistage Manufacturing using Classification and Association Rule Mining Techniques." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1535382878246765.
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Padhye, Manoday D. "Use of data mining for investigation of crime patterns." Morgantown, W. Va. : [West Virginia University Libraries], 2006. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=4836.
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Thesis (M.S.)--West Virginia University, 2006.<br>Title from document title page. Document formatted into pages; contains viii, 108 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 80-81).
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Liao, Yuan-Fong, and 廖原豐. "Causal Association Rule Mining." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/sy5ufc.
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碩士<br>國立中央大學<br>資訊管理研究所<br>94<br>This thesis mainly probes into the causality among the investment problems of the stock market to do for the experimental subject of this research. We focus on discussing how about to promote the performance of investment. If we want to promote the performance of investment, we must understand the causality among the factor which influences the performance and performance observing value. we will utilize the method of association rule of data mining to help to look for association rules about causality among the technological indicators which influences the performance and performance observing value (ex. the reversal point of the stock price). We call these rules as Causal Association Rules. We can make these rules up into the tactics of securities trading.
In the past, many scholars proposed a lot of methods of association rules, but these methods will produce a large number of large itemsets. So that there are too many rules and it is difficult to assess the interesting of rules and relatively inefficient. So we propose a CFP algorithm structure which mainly improve FP-Growth algorithm to reduce mining the unnecessary large itemsets and enable only producing the interesting causal association rules efficiently.
The common data dispersed methods now have equal width interval and equal frequency interval. But when investors pass in and out stock market to buy or sell stocks, they usually reference the aggregate value of technological indicators. So we propose equal width aggregate interval and equal frequency aggregate interval. These two data dispersed methods can also support mining causal association rules with level crossing so that we can mine more interesting rules. As the result of t test, the performance of our algorithm is better than FP-growth algorithm apparently. We also find the CFP algorithm is suitable for mining large-scalar database.
We arrange causal association rules in an order by different point of view to analysis so as to offer investors assistance in arrangements of investment tactics and the reference of to avoid the loss.
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Li, Shenzhi. "Higher order association rule mining." 2010. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3389963.
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Lin, Shih Hsiang, and 林士翔. "DARM: Doughnut-shaped Association Rule Mining." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/54386438560648611106.
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碩士<br>長庚大學<br>資訊管理學研究所<br>97<br>This is the age of “Information Explosion”. We can easier to get more and more information. Information visualization research is to be valuable for conveniently presenting the infinite information. It is often seen the information visualization products like maps, signs, graphs in our life. Information visualization can also use in data mining methodology. Data mining is often called knowledge discovery. Association rule mining is the most famous data mining method. Association rule mining is used to discover all associations among items. However, user can not hold the important item fast and exactly by text. We propose an association rule algorithm which use doughnut shapes to present association rule.
DARM(Doughnut-shaped association rule mining) includes a overview circle and lots of detail circles which produced by items. DARM let user understand the mining step easily. User can use their self-knowledge and self-experience to participate in the process. Most importantly, we use the simple and clear doughnut shapes let user realize the database overview and all associations among items rapidly.
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Cheng, Yung-Hsiung, and 鄭永雄. "A study of association rule mining algorithms." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/12205682895999423189.
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碩士<br>義守大學<br>資訊管理學系碩士班<br>95<br>In recent years, the techniques of Data Mining has already become one of the rather popular research subjects. Its purpose is to mining meaningful information from the database, and provides it to the administrator for decision making. In past relevant research, many algorithms were proposed to improve the effect of association rule currently. These methods are to reduce the computation of non-correlation itemsets to save the CPU time, or to reduces the information search frequency to save the I/O cost, or even to improve storage configuration and access method to promote whole effect. These algorithms each have their own advantage. but lack of synthetically inter-communication. If the user is to mining an unknown database, it will be difficult to determine which algorithm provides the best effect, therefore we must consider the applicability of the association rule of data mining algorithm in order to mine data more effectively and obtain useful information.
The research inquires into presently five association rule algorithms, and uses them individually to process several real databases. And then analyze these experiment data to see each algorithm’s pros and cons and its applicable type of database characteristics. We then carry on to process the Apriori algorithm, Frequent-pattern growth(FP-growth) algorithm, Dynamic Itemset Counting(DIC) algorithm, the Pruning of the Direct Hashing(DHP) algorithm and the LCM-freq algorithm according to the characteristic of database, obtain the processed data from several database and organize them. Finally, we wish to suggest the users use more effective association rules of data mining algorithm.
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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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Jin, Weiqing. "Fuzzy classification based on fuzzy association rule mining." 2004. http://www.lib.ncsu.edu/theses/available/etd-12072004-130619/unrestricted/etd.pdf.
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Liu, Po-Ting, and 劉柏廷. "Association Rule Based Relational Mining for Stock Trading." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/99939684843737594402.
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碩士<br>國立中央大學<br>資訊管理研究所<br>95<br>When it comes to analyzing numerical data by Association Rule, we have to disperse those numerical data before we start to use them as a data mining source data. The common data dispersed methods are “equal width interval” and “equal frequency interval”. We categorize these two methods into “absolute”, because both of them classify different values into each interval with the same length. In practice, equal width interval and equal frequency interval are not necessary the suitable way to deal with all kinds of data. For example, the usage of many popular and famous technical analysis indicators is considered “relative-comparison”, rather than “absolute- comparison”. Therefore, if we simply treat all kinds of data as “absolute-comparison” data without thinking about whether those data have “relative-comparison” characteristics in nature, we may lead to information loss because we ignore some important features in those data.
For this reason, we propose a concept of “relative-type comparative relation” which is an alternative to “equal width interval” and “equal frequency interval” for data preprocessing. Through “relative-comparison” we can transfer numerical data to data mining source data in a more appropriate way that make the source data more similar into the numerical data in meaning, so that we can reduce information loss and enhance the result of data mining.
After applying “relative-comparison” to association rule data mining, we use CBA(Classification Based on Associations) to classify and predict the target data. CBA can be divided in two steps which are “rule simplification” and “collective evaluation.” “Rule simplification” eliminates those redundant rules and integrates those general rules for classification. “Collective evaluation” uses the total confidence of screened rules to classify and predict the target data and enhance the accuracy of classification and prediction.
The experimental data is extracted from American stock trading data form 2003 to 2006. The results of the experiments show that the application of “relative-comparison” does improve the precision of stock price estimation. After we implement “rule simplification” and “collective evaluation” in the experiments, we improve the precision rate to a higher level.
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Wu, Wei-Mao, and 吳幃楙. "The Research of Multilevel Association Rule Mining Model." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/62557825511482111060.
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碩士<br>僑光科技大學<br>資訊科技研究所<br>102<br>Nowadays, data mining is more and more mature than before by the internet and technology development. The main target of data mining is in order to collect the potentially valuable information efficiently from a large database. Moreover, the primary purpose of association rule is try to find a related product by transaction. For example, it can be found an association rule which is "80% of customers purchase the computers while also buy the screens".
This essay is to construct a model of multilevel association rule mining model, while analysis and discuss deeply and completely. Because the original model only focus on the same level, this essay will discuss the integrity of multilevel association rule. It not only the related of the same level, but also add the filtering simple information in the original multilevel association model. This action can produce the different level correlations. The result of the analysis in this essay can help industrial companies to find the useful marketing strategy and provide the customized services for customers, while enhancing the overall sales performance.
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Chen, Chao-Hsun, and 陳朝勳. "Performance Comparison of Parallelized Association-rule Mining Algorithms." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/559wvf.
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碩士<br>國立臺中科技大學<br>資訊工程系碩士班<br>102<br>Getting information becomes easier and selecting goods becomes more diverse due to the growth of internet and the mature database technology. Consumers want to decrease the searching time when buying goods. For the suppliers, they want to place the goods together depending on relevance for effectively selling goods. Cloud technology has become a hot topic in the industry circle as rapid development of computer hardware technology. How to effectively decrease computing time and analyze big data when handling more and more information? However, it becomes an important topic. In this study, through parallel MapReduce framework design, we perform association rules of data mining by analyzing the two famous mining algorithms (Apriori and PIETM). We find that the performances of Apriori and PIETM algorithm have their own pros and cons in parallel MapReduce framework design from our experiment.
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HUANG, YUN-REN, and 黃韻仁. "Blood Glucose Association Rule Mining for Elderly People." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/zejxbv.
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碩士<br>明志科技大學<br>工業工程與管理系碩士班<br>106<br>High blood glucose occurs when the body has too little insulin or is unable to use insulin properly. High blood glucose is one of the main symptoms of diabetes. Diabetes is particularly prevalent in the elderly, and the consequence of high blood glucose in the elderly is most likely diabetes. Therefore, blood glucose control is essential for the health of the elderly. Glycosylated hemoglobin (HbA1c) can be used to detect chronic hyperglycemia, but the elderly rarely monitor their blood glucose at home and often don't know their blood glucose status until they get a health examination at the hospital. On the other hand, due to changes of diet and lifestyle in Taiwanese, excessive and uneven diet combined with sedentary and lack of exercise lead to obesity and chronic diseases. high blood glucose is one of the important chronic diseases. Therefore, the adjustment of lifestyle is important for the prevention of high blood glucose .
This study attempts to discover the association rules between elderly health examination data and lifestyle habits and the blood glucose level. First, the data of health examination, living habits and blood glucose of the elderly is collected. Second, the association rules of health examination and lifestyle to blood glucose is mined by using the Predictive Apriori (PA) algorithm.Third, the mined association rules are filtered with with support, confidence, lift and conviction. The support and confidence are used to illustrate the rules of effectiveness, and the lift and conviction are used as the indicators of importance.Finally, the filtered association rules are compared each otherto ensure the importance and uniqueness of related factors of blood glucose. With these association rules, physicians and the elderly can obtain additional references for high blood glucose diagnosis and pay more attention to related causes and complications of high blood glucose to reduce the risk of suffering from high blood sugar.
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Wu, Chin-Wei, and 吳靜薇. "Association Rule Mining For Enrollment Grade And Graduate." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/48273824059652510501.
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碩士<br>國立高雄師範大學<br>資訊教育研究所<br>101<br>The object of this research is to study the relationship of the entrance score, the admission types, and school achievement for the profession education schools. The research data is based on the 98、99、100 academic year's result of one private profession education school in Tainan. The relationship principles of Data Mining is used to analyze the school achievement, admission types, entrance score, gender, department, entrance identity, and the graduated junior high school for three academic years. Improve thorough understanding for the above factors, and can be a decision reference for school to recruit students.
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Chiu, Shih-Chun, and 邱士軍. "Implementataion and Performance Evaluation of Association Rule Mining Algorithms." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/77869041005439613437.
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碩士<br>國立臺灣科技大學<br>資訊管理系<br>90<br>Mining of association rules is a popular research area in data mining. Many mining algorithms for association rules have been proposed in the recent years. Every author of the mining algorithm claims that his algorithm is the best under some specific conditions. A fair comparison serves as a guide for choosing the right mining algorithm for a given specific condition. Unfortunately, no fair third party has conducted comprehensive comparisons among the association rule mining algorithms. In this thesis, we perform performance comparisons on five well-known algorithms. Among them are Apriori, Boolean, FP-Growth, Maxminer and DIC algorithms. We implemented several versions for each algorithm. Then, we choose the most efficient implementation among our implementations and the implementation provided directly by the original author if one is available. We also describe the details of our implementations. To compare the performance of the algorithms, we use three synthetic transactional databases generated by the IBM dataset generator and the FoodMart database, a real transactional database from SQL Server. The three synthetic databases are T5I2, T10I4 and T20I6. They have different mean transaction length and mean frequent itemsets length.
Experiments show that no algorithm prevails in all circumstances. The Apriori algorithm and the DIC algorithm prevail when the minimum support is high and, therefore, less computation time is needed. On the other hand, the Boolean algorithm and the FP-Growth algorithm scale up well in the sense that they prevail under low minimum support. Furthermore, the Boolean algorithm and the FP-Growth algorithm significantly outperform other algorithms when the mean transaction length is long. Besides, we also found that the memory size occupied by the FP-tree is at least as large as the transactional database itself.
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Shu, Ching Cao, and 卓叔靜. "Hypothesis Testing of Association-Rule Confidence in Data Mining." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/54816177313437814043.
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碩士<br>國立臺灣科技大學<br>資訊管理系<br>90<br>A decision rule for mining association rule is developed by estimating the distribution
of the association-rule confidence estimator.
The type I error of hypothesis testing is shown to be controlled when the association-rule
confidence is at the prespecified minimum confidence.
The critical values for testing the hypothesis about the association-rule confidence
are determined by estimating the distribution of the confidence estimator through Monte Carlo
simulation.
The results are useful for mining association rule when using random sampling.
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Chien, Peng Wang, and 王建鵬. "Find the General Rule of Data Mining Association Rules." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/08735074145658888662.
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碩士<br>萬能科技大學<br>資訊管理研究所<br>99<br>At present the application of association rule mining and research, to exchange products generated discussion targeted mostly clustered, and in the exploration process and output that, there is no a general rule of representation, usually in a unique way or the text description . This study proposes a concept of transactions by participants in the association rule mining as an object. For association rule mining applications more flexible, to entities associated with the set methodology for the extension of a graphical representation, so that regardless of the implementation of the method, the can be simple and clear expression, and association rule mining to fully describe the various restrictions , regardless of entity-relationship structure, star structure, snow structure, can be described as a class can be summarized, and describe the relationship between different induction levels. Another object via the specified mining, exploration using different trading partners, meaning more like mining.