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1
Hammoud, Suhel. "MapReduce network enabled algorithms for classification based on association rules". Thesis, Brunel University, 2011. http://bura.brunel.ac.uk/handle/2438/5833.
Testo completoAbstract (sommario):
There is growing evidence that integrating classification and association rule mining can produce more efficient and accurate classifiers than traditional techniques. This thesis introduces a new MapReduce based association rule miner for extracting strong rules from large datasets. This miner is used later to develop a new large scale classifier. Also new MapReduce simulator was developed to evaluate the scalability of proposed algorithms on MapReduce clusters. The developed associative rule miner inherits the MapReduce scalability to huge datasets and to thousands of processing nodes. For finding frequent itemsets, it uses hybrid approach between miners that uses counting methods on horizontal datasets, and miners that use set intersections on datasets of vertical formats. The new miner generates same rules that usually generated using apriori-like algorithms because it uses the same confidence and support thresholds definitions. In the last few years, a number of associative classification algorithms have been proposed, i.e. CPAR, CMAR, MCAR, MMAC and others. This thesis also introduces a new MapReduce classifier that based MapReduce associative rule mining. This algorithm employs different approaches in rule discovery, rule ranking, rule pruning, rule prediction and rule evaluation methods. The new classifier works on multi-class datasets and is able to produce multi-label predications with probabilities for each predicted label. To evaluate the classifier 20 different datasets from the UCI data collection were used. Results show that the proposed approach is an accurate and effective classification technique, highly competitive and scalable if compared with other traditional and associative classification approaches. Also a MapReduce simulator was developed to measure the scalability of MapReduce based applications easily and quickly, and to captures the behaviour of algorithms on cluster environments. This also allows optimizing the configurations of MapReduce clusters to get better execution times and hardware utilization.
2
Abdelhamid, Neda. "Deriving classifiers with single and multi-label rules using new Associative Classification methods". Thesis, De Montfort University, 2013. http://hdl.handle.net/2086/10120.
Testo completoAbstract (sommario):
Associative Classification (AC) in data mining is a rule based approach that uses association rule techniques to construct accurate classification systems (classifiers). The majority of existing AC algorithms extract one class per rule and ignore other class labels even when they have large data representation. Thus, extending current AC algorithms to find and extract multi-label rules is promising research direction since new hidden knowledge is revealed for decision makers. Furthermore, the exponential growth of rules in AC has been investigated in this thesis aiming to minimise the number of candidate rules, and therefore reducing the classifier size so end-user can easily exploit and maintain it. Moreover, an investigation to both rule ranking and test data classification steps have been conducted in order to improve the performance of AC algorithms in regards to predictive accuracy. Overall, this thesis investigates different problems related to AC not limited to the ones listed above, and the results are new AC algorithms that devise single and multi-label rules from different applications data sets, together with comprehensive experimental results. To be exact, the first algorithm proposed named Multi-class Associative Classifier (MAC): This algorithm derives classifiers where each rule is connected with a single class from a training data set. MAC enhanced the rule discovery, rule ranking, rule filtering and classification of test data in AC. The second algorithm proposed is called Multi-label Classifier based Associative Classification (MCAC) that adds on MAC a novel rule discovery method which discovers multi-label rules from single label data without learning from parts of the training data set. These rules denote vital information ignored by most current AC algorithms which benefit both the end-user and the classifier's predictive accuracy. Lastly, the vital problem related to web threats called 'website phishing detection' was deeply investigated where a technical solution based on AC has been introduced in Chapter 6. Particularly, we were able to detect new type of knowledge and enhance the detection rate with respect to error rate using our proposed algorithms and against a large collected phishing data set. Thorough experimental tests utilising large numbers of University of California Irvine (UCI) data sets and a variety of real application data collections related to website classification and trainer timetabling problems reveal that MAC and MCAC generates better quality classifiers if compared with other AC and rule based algorithms with respect to various evaluation measures, i.e. error rate, Label-Weight, Any-Label, number of rules, etc. This is mainly due to the different improvements related to rule discovery, rule filtering, rule sorting, classification step, and more importantly the new type of knowledge associated with the proposed algorithms. Most chapters in this thesis have been disseminated or under review in journals and refereed conference proceedings.
3
Palanisamy, Senthil Kumar. "Association rule based classification". Link to electronic thesis, 2006. http://www.wpi.edu/Pubs/ETD/Available/etd-050306-131517/.
Testo completoAbstract (sommario):
Thesis (M.S.)--Worcester Polytechnic Institute.Keywords: Itemset Pruning, Association Rules, Adaptive Minimal Support, Associative Classification, Classification. Includes bibliographical references (p.70-74).
4
Bilal, Kirkici. "Words And Rules In L2 Processing: An Analysis Of The Dual-mechanism Model". Phd thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12605980/index.pdf.
Testo completoAbstract (sommario):
The nature of the mental representation and processing of morphologically complex words has constituted one of the major points of controversy in psycholinguistic research over the past two decades. The Dual-Mechanism Model defends the necessity of two separate mechanisms for linguistic processing, an associative memory and a rule-system, which account for the processing of irregular and regular word forms, respectively. The purpose of the present study was to analyse the validity of the claims of the Dual-Mechanism Model for second language (L2) processing in order to contribute to the accumulating but so far equivocal knowledge concerning L2 processing. A second purpose of the study was to find out whether L2 proficiency could be identified as a determining factor in the processing of L2 morphology.
Two experiments (a lexical decision task on the English past tense and a elicited production task on English lexical compounds) were run with 22 low-proficiency and 24 high-proficiency first language (L1) Turkish users of L2 English and with 6 L1 speakers of English. The results showed that the regular-irregular dissociation predicted by the Dual-Mechanism Model was clearly evident in the production of English lexical compounds for all three subject groups. A comparatively weaker dissociation coupled with intricate response patterns was found in the processing of the English past tense, though possibly because of a number of confounding factors that were not sufficiently controlled. In addition, direct comparisons of the L2 groups displayed a remarkable effect of L2 proficiency on L2 morphological processing.
5
Дрозд, С. А. "Статистично-ймовірнісна оцінка корупційних ризиків в Україні". Master's thesis, Сумський державний університет, 2019. http://essuir.sumdu.edu.ua/handle/123456789/75918.
Testo completoAbstract (sommario):
У роботі досліджено ймовірність скоєння корупційних злочинів залежно від сфери діяльності та посади можливих злочинців також ймовірність того, що при скоєнні певного корупційного злочину додатково може бути скоєно інший корупційний злочин. Основною метою цього дослідження є розробка моделі статистично-ймовірнісної оцінки корупційних ризиків в Україні.In the work the probability of Commission of corruption crimes depending on the sphere of activity and a position of possible criminals is investigated also probability of that at Commission of a certain corruption crime in addition other corruption crime can be committed. The main purpose of this study is to develop a model of statistical and probabilistic assessment of corruption risks in Ukraine.
6
Lombardini, Alessandro. "Estrazione di Correlazioni Medicali da Social Post non Etichettati con Language Model Neurali e Data Clustering". Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020.
Cerca il testo completoAbstract (sommario):
La progressiva informatizzazione della società a cui il mondo contemporaneo sta assistendo, ha generato un radicale cambiamento nelle abitudini delle persone, le quali oggi giorno trascorrono sempre più tempo online e creano reti di conoscenza prima inimmaginabili. Tale cambiamento ha coinvolto, nel suo avanzare, anche gli individui affetti da malattie di varia natura. In particolare, la scarsa disponibilità di informazioni che caratterizza alcuni contesti medici, unita al bisogno di dialogare con altre persone aventi la medesima problematica, ha determinato negli ultimi anni una forte crescita di comunità sulle piattaforme social, all’interno delle quali vengono scambiati dettagli rispetto a trattamenti, centri specializzati e dottori. In questo senso, i social network sono diventati il luogo in cui i pazienti sono più propensi a condividere le proprie esperienze e opinioni maturate durante il corso della propria malattia. Questa tesi nasce dalla consapevolezza del valore di tali dati e dalla volontà di consentire un ragionamento logico deduttivo al di sopra di essi. Nello specifico, si intende estrarre — con un approccio non supervisionato, mediante l’uso di language model neurali e data clustering — le correlazioni semantiche racchiuse nell’elevata quantità di testo generato dagli utenti attraverso interazioni social, prendendo l’Acalasia Esofagea come caso di studio.
7
Qing, Yang. "Pruning and summarizing discovered time series association rules". Thesis, Mittuniversitetet, Avdelningen för informationssystem och -teknologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-31828.
Testo completoAbstract (sommario):
Sensors are widely used in all aspects of our daily life including factories, hospitals and even our homes. Discovering time series association rules from sensor data can reveal the potential relationship between different sensors which can be used in many applications. However, the time series association rule mining algorithms usually produce rules much more than expected. It’s hardly to under-stand, present or make use of the rules. So we need to prune and summarize the huge amount of rules. In this paper, a two-step pruning method is proposed to reduce both the number and redundancy in the large set of time series rules. Be-sides, we put forward the BIGBAR summarizing method to summarize the rules and present the results intuitively.
8
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/.
Testo completoAbstract (sommario):
Thesis (M.S.) -- Worcester Polytechnic Institute.Keywords: mining complex data; temporal association rules; computer system performance; stock market analysis; sleep disorder data. Includes bibliographical references (p. 79-85).
9
Koh, Yun Sing, e n/a. "Generating sporadic association rules". University of Otago. Department of Computer Science, 2007. http://adt.otago.ac.nz./public/adt-NZDU20070711.115758.
Testo completoAbstract (sommario):
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.
10
Krasniuk, Maxim, e Svitlana Krasnyuk. "Association rules in finance management". Thesis, Primedia eLaunch & European Scientific Platform, 2021. https://er.knutd.edu.ua/handle/123456789/18974.
Testo completo
11
Fabian, Jaroslav. "Využití technik Data Mining v různých odvětvích". Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2014. http://www.nusl.cz/ntk/nusl-224335.
Testo completoAbstract (sommario):
This master’s thesis concerns about the use of data mining techniques in banking, insurance and shopping centres industries. The thesis theoretically describes algorithms and methodology CRISP-DM dedicated to data mining processes. With usage of theoretical knowledge and methods, the thesis suggests possible solution for various industries within business intelligence processes.
12
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.
Testo completoAbstract (sommario):
Thesis (M. Phil.)--Chinese University of Hong Kong, 1998.Description based on contents viewed Mar. 13, 2007; title from title screen. Includes bibliographical references (p. 99-103). Also available in print.
13
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.
Testo completo
14
王漣 e Lian Wang. "A study on quantitative association rules". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1999. http://hub.hku.hk/bib/B31223588.
Testo completo
15
Matthews, Stephen. "Learning lost temporal fuzzy association rules". Thesis, De Montfort University, 2012. http://hdl.handle.net/2086/8257.
Testo completoAbstract (sommario):
Fuzzy association rule mining discovers patterns in transactions, such as shopping baskets in a supermarket, or Web page accesses by a visitor to a Web site. Temporal patterns can be present in fuzzy association rules because the underlying process generating the data can be dynamic. However, existing solutions may not discover all interesting patterns because of a previously unrecognised problem that is revealed in this thesis. The contextual meaning of fuzzy association rules changes because of the dynamic feature of data. The static fuzzy representation and traditional search method are inadequate. The Genetic Iterative Temporal Fuzzy Association Rule Mining (GITFARM) framework solves the problem by utilising flexible fuzzy representations from a fuzzy rule-based system (FRBS). The combination of temporal, fuzzy and itemset space was simultaneously searched with a genetic algorithm (GA) to overcome the problem. The framework transforms the dataset to a graph for efficiently searching the dataset. A choice of model in fuzzy representation provides a trade-off in usage between an approximate and descriptive model. A method for verifying the solution to the hypothesised problem was presented. The proposed GA-based solution was compared with a traditional approach that uses an exhaustive search method. It was shown how the GA-based solution discovered rules that the traditional approach did not. This shows that simultaneously searching for rules and membership functions with a GA is a suitable solution for mining temporal fuzzy association rules. So, in practice, more knowledge can be discovered for making well-informed decisions that would otherwise be lost with a traditional approach.
16
Wang, Lian. "A study on quantitative association rules /". Hong Kong : University of Hong Kong, 1999. http://sunzi.lib.hku.hk/hkuto/record.jsp?B2118561X.
Testo completo
17
Hahsler, Michael, e Radoslaw Karpienko. "Visualizing association rules in hierarchical groups". Springer, 2016. http://dx.doi.org/10.1007/s11573-016-0822-8.
Testo completoAbstract (sommario):
Association rule mining is one of the most popular data mining methods. However, mining association rules often results in a very large number of found rules, leaving the analyst with the task to go through all the rules and discover interesting ones. Sifting manually through large sets of rules is time consuming and strenuous. Although visualization has a long history of making large amounts of data better accessible using techniques like selecting and zooming, most association rule visualization techniques are still falling short when it comes to large numbers of rules. In this paper we introduce a new interactive visualization method, the grouped matrix representation, which allows to intuitively explore and interpret highly complex scenarios. We demonstrate how the method can be used to analyze large sets of association rules using the R software for statistical computing, and provide examples from the implementation in the R-package arulesViz. (authors' abstract)
18
Pietruszewski, Przemyslaw. "Association rules analysis for objects hierarchy". Thesis, Blekinge Tekniska Högskola, Avdelningen för programvarusystem, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3512.
Testo completoAbstract (sommario):
Association rules are one of the most popular methods of data mining. This technique allows to discover interesting dependences between objects. The thesis concerns on association rules for hierarchy of objects. As a multi–level structure is used DBLP database, which contains bibliographic descriptions of scientific papers conferences and journals in computer science. The main goal of thesis is investigation of interesting patterns of co-authorship with respect to different levels of hierarchy. To reach this goal own extracting method is proposed.p.pietruszewski@op.pl
19
Goulbourne, Graham. "Tree algorithms for mining association rules". Thesis, University of Liverpool, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.250218.
Testo completoAbstract (sommario):
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.
20
Jabarnejad, Masood. "An Improved Organization Method For Association Rules And A Basis For Comparison Of Methods". Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12611960/index.pdf.
Testo completoAbstract (sommario):
In large data, set of mined association rules are typically large in number and hard to interpret. Some grouping and pruning methods have been developed to make rules more understandable. In this study, one of these methods is modified to be more effective and more efficient in applications including low thresholds for support or confidence, such as association analysis of product/process quality improvement. Results of experiments on benchmark datasets show that the proposed method groups and prunes more rules.
In the literature, many rule reduction methods, including grouping and pruning methods, have been proposed for different applications. The variety in methods makes it hard to select the right method for applications such those of quality improvement. In this study a novel performance comparison basis is introduced to address this problem. It is applied here to compare the improved method to the original one. The introduced basis is tailored for quality data, but is flexible and can be changed to be applicable in other application domains.
21
Unal, Calargun Seda. "Fuzzy Association Rule Mining From Spatio-temporal Data: An Analysis Of Meteorological Data In Turkey". Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609308/index.pdf.
Testo completoAbstract (sommario):
Data mining is the extraction of interesting non-trivial, implicit, previously unknown and
potentially useful information or patterns from data in large databases. Association rule
mining is a data mining method that seeks to discover associations among transactions encoded
within a database. Data mining on spatio-temporal data takes into consideration the
dynamics of spatially extended systems for which large amounts of spatial data exist, given
that all real world spatial data exists in some temporal context. We need fuzzy sets in mining
association rules from spatio-temporal databases since fuzzy sets handle the numerical
data better by softening the sharp boundaries of data which models the uncertainty embedded
in the meaning of data. In this thesis, fuzzy association rule mining is performed
on spatio-temporal data using data cubes and Apriori algorithm. A methodology is developed
for fuzzy spatio-temporal data cube construction. Besides the performance criteria
interpretability, precision, utility, novelty, direct-to-the-point and visualization are defined
to be the metrics for the comparison of association rule mining techniques. Fuzzy association
rule mining using spatio-temporal data cubes and Apriori algorithm performed
within the scope of this thesis are compared using these metrics. Real meteorological data
(precipitation and temperature) for Turkey recorded between 1970 and 2007 are analyzed
using data cube and Apriori algorithm in order to generate the fuzzy association rules.
22
Delpisheh, Elnaz, e 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.
Testo completoAbstract (sommario):
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.viii, 85 leaves : ill. ; 29 cm
23
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.
Testo completo
24
李守敦 e Sau-dan Lee. "Maintenance of association rules in large databases". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1997. http://hub.hku.hk/bib/B31215531.
Testo completo
25
Savulionienė, Loreta. "Association rules search in large data bases". Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2014. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2014~D_20140519_102242-45613.
Testo completoAbstract (sommario):
The impact of information technology is an integral part of modern life. Any activity is related to information and data accumulation and storage, therefore, quick analysis of information is necessary. Today, the traditional data processing and data reports are no longer sufficient. The need of generating new information and knowledge from given data is understandable; therefore, new facts and knowledge, which allow us to forecast customer behaviour or financial transactions, diagnose diseases, etc., can be generated applying data mining techniques. The doctoral dissertation analyses modern data mining algorithms for estimating frequent sub-sequences and association rules. The dissertation proposes a new stochastic algorithm for mining frequent sub-sequences, its modifications SDPA1 and SDPA2 and stochastic algorithm for discovery of association rules, and presents the evaluation of the algorithm errors. These algorithms are approximate, but allow us to combine two important tests, i.e. time and accuracy. The algorithms have been tested using real and simulated databases.Informacinių technologijų įtaka neatsiejama nuo šiuolaikinio gyvenimo. Bet kokia veiklos sritis yra susijusi su informacijos, duomenų kaupimu, saugojimu. Šiandien nebepakanka tradicinio duomenų apdorojimo bei įvairių ataskaitų formavimo. Duomenų tyrybos technologijų taikymas leidžia iš turimų duomenų išgauti naujus faktus ar žinias, kurios leidžia prognozuoti veiklą, pavyzdžiui, pirkėjų elgesį ar finansines tendencijas, diagnozuoti ligas ir pan. Disertacijoje nagrinėjami duomenų tyrybos algoritmai dažniems posekiams ir susietumo taisyklėms nustatyti. Disertacijoje sukurtas naujas stochastinis dažnų posekių paieškos algoritmas, jo modifikacijos SDPA1, SDPA2 ir stochastinis susietumo taisyklių nustatymo algoritmas bei pateiktas šių algoritmų paklaidų įvertinimas. Šie algoritmai yra apytiksliai, tačiau leidžia suderinti du svarbius kriterijus laiką ir tikslumą. Šie algoritmai buvo testuojami naudojant realias bei imitacines duomenų bazes.
26
Lee, Sau-dan. "Maintenance of association rules in large databases /". Hong Kong : University of Hong Kong, 1997. http://sunzi.lib.hku.hk/hkuto/record.jsp?B19003250.
Testo completo
27
Hahsler, Michael, e Kurt Hornik. "New Probabilistic Interest Measures for Association Rules". Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 2006. http://epub.wu.ac.at/1286/1/document.pdf.
Testo completoAbstract (sommario):
Mining association rules is an important technique for discovering meaningful patterns in transaction databases. Many different measures of interestingness have been proposed for association rules. However, these measures fail to take the probabilistic properties of the mined data into account. In this paper, we start with presenting a simple probabilistic framework for transaction data which can be used to simulate transaction data when no associations are present. We use such data and a real-world database from a grocery outlet to explore the behavior of confidence and lift, two popular interest measures used for rule mining. The results show that confidence is systematically influenced by the frequency of the items in the left hand side of rules and that lift performs poorly to filter random noise in transaction data. Based on the probabilistic framework we develop two new interest measures, hyper-lift and hyper-confidence, which can be used to filter or order mined association rules. The new measures show significant better performance than lift for applications where spurious rules are problematic.Series: Research Report Series / Department of Statistics and Mathematics
28
Changchien, Ya-Wen, e 張簡雅文. "Mining Associative Classification Rules from Numerical Data". Thesis, 2009. http://ndltd.ncl.edu.tw/handle/62880191177500339708.
Testo completoAbstract (sommario):
博士國立中央大學
資訊管理研究所
98
Associative classification, one of data mining techniques, is a classification system based on associative classification rules. Although associative classification is more accurate than traditional classification approaches, such as C4.5 and ILP, it cannot handle numerical data and its relations. Therefore, an ongoing research problem is how to build associative classifiers from numerical data. Inductive logic programming (ILP), one of traditional classification approaches, has great capability of relations representation, and flexibility for problem representation and problem-specific constraints. However, it is not suitable for noisy environment and has weak facilities for processing numerical data, including unsatisfactory learning time with a large number of arguments in the relations. A phenotypic genetic algorithm(PGA) with multi-level phonotypic encoding structure is proposed to solve the problems in the ILP system. This structure has great capability of relations representation between numerical data and is used for relations encoding between numerical data in associative classification rules mining. The experiment results show that the proposed approach(GA-ACR) has high prediction accuracy and is highly competitive when compared with the data distribution method.
29
An, Pao-Ying, e 安寶楹. "Mining Associative Sequential Rules for Image Classification". Thesis, 2002. http://ndltd.ncl.edu.tw/handle/96293014152058508425.
Testo completoAbstract (sommario):
碩士國立臺灣師範大學
資訊教育研究所
90
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.
30
Hung, Tzu-hsuan, e 洪子軒. "Using Decision Tree to Summarize Associative Classification Rules". Thesis, 2007. http://ndltd.ncl.edu.tw/handle/07372134788817702281.
Testo completoAbstract (sommario):
碩士國立中央大學
資訊管理研究所
95
Association rule mining is one of the most popular areas in data mining. It is to discover items that co-occur frequently within a set of transactions, and to discover rules based on these co-occurrence relations. Association rules have been adopted into classification problem for years (associative classification). However, once rules have been generated, their lacking of organization causes readability problem, i.e., it is difficult for user to analyze them and understand the domain. To resolve this weakness, our work presented two algorithms that can use decision tree to summarize associative classification rules. As a classification model, it connects the advantages of both associative classification and decision tree. On one hand, it is a more readable, compact, well-organized form and easier to use when compared to associative classification. On the other hand, it is more accurate than traditional TDIDT (abbreviated from Top-Down Induction of Decision Trees) classification algorithm.
31
Venkatesh, Santosh Subramanyam. "Linear Maps with Point Rules: Applications to Pattern Classification and Associative Memory". Thesis, 1987. https://thesis.library.caltech.edu/883/9/Venkatesh_ss_1987.pdf.
Testo completoAbstract (sommario):
Generalisations of linear discriminant functions are introduced to tackle problems in pattern classification, and associative memory. The concept of a point rule is defined, and compositions of global linear maps with point rules are incorporated in two distinct structural forms—feedforward and feedback—to increase classification flexibility at low increased complexity. Three performance measures are utilised, and measures of consistency established.
Feedforward pattern classification systems based on multi-channel machines are introduced. The concept of independent channels is defined and used to generate independent features. The statistics of multi-channel classifiers are characterised, and specific applications of these structures are considered. It is demonstrated that image classification invariant to image rotation and shift is possible using multi-channel machines incorporating a square-law point rule. The general form of rotation invariant classifier is obtained. The existence of optimal solutions is demonstrated, and good sub-optimal systems are introduced, and characterised. Threshold point rules are utilised to generate a class of low-cost binary filters which yield excellent classification performance. Performance degradation is characterised as a function of statistical side-lobe fluctuations, finite system space-bandwidth, and noise.
Simplified neural network models are considered as feedback systems utilising a linear map and a threshold point rule. The efficacy of these models is determined for the associative storage and recall of memories. A precise definition of the associative storage capacity of these structures is provided. The capacity of these networks under various algorithms is rigourously derived, and optimal algorithms proposed. The ultimate storage capacity of neural networks is rigourously characterised. Extensions are considered incorporating higher-order networks yielding considerable increases in capacity.
32
Tiple, Pedro Santos. "Tool for discovering sequential patterns in financial markets". Master's thesis, 2014. http://hdl.handle.net/10362/14091.
Testo completoAbstract (sommario):
The goal of this thesis is the study of a tool that can help analysts in finding sequential patterns. This tool will have a focus on financial markets. A study will be made on how new and relevant knowledge can be mined from real life information, potentially giving investors, market analysts, and economists new basis to make informed decisions.
The Ramex Forum algorithm will be used as a basis for the tool, due to its ability to find sequential patterns in financial data. So that it further adapts to the needs of the thesis, a study of relevant improvements to the algorithm will be made. Another important aspect of this algorithm is the way that it displays the patterns found, even with good results it is difficult to find relevant patterns among all the studied samples without a proper result visualization component. As such, different combinations of parameterizations and ways
to visualize data will be evaluated and their influence in the analysis of those patterns will be discussed.
In order to properly evaluate the utility of this tool, case studies will be performed as a final test. Real information will be used to produce results and those will be evaluated in regards to their accuracy, interest, and relevance.
33
Lo, Min-Lung, e 羅閔隆. "The Experience Rule for Giving Association Rules Threshold". Thesis, 2004. http://ndltd.ncl.edu.tw/handle/43969239126914475464.
Testo completoAbstract (sommario):
碩士大葉大學
資訊管理學系碩士班
92
It is very important technique to find the association rule from database transactions about the data mining. What is called association rule which is to find interrelationship in a database. For the reasons the rule must be meaningful, the rule must be greater than the threshold of support and confidence. How large the threshold should be? It must be given by an expert usually. And there is no any normal regulations can be obeyed. So in our research we will try to formulate the threshold by percentile. By this method, we expect to have more meaningful association rules. In this paper, we define the threshold by the percentile. We assume the percentiles is depend on mean, skewness, kurtosis and others statistical parameter. We try to use these statistical parameters to find an experience formula, and use this experience rule may obtain optimal threshold quickly. We expect to find a using meaningfull and reliable with the experience formula.
34
Chien, Peng Wang, e 王建鵬. "Find the General Rule of Data Mining Association Rules". Thesis, 2011. http://ndltd.ncl.edu.tw/handle/08735074145658888662.
Testo completoAbstract (sommario):
碩士萬能科技大學
資訊管理研究所
99
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.
35
LIN, MING-HUNG, e 林銘泓. "Exploringthe Distribution Rules of Aggregate Using Data Mining Association Rule". Thesis, 2016. http://ndltd.ncl.edu.tw/handle/00708958833560184595.
Testo completoAbstract (sommario):
碩士萬能科技大學
資訊管理研究所在職專班
104
Aggregate of ready-mixed concrete from the shipping dock to bulk cargo, then vehicle distribution to various ready-mix plant, temporary storage yard. Provided that the transportation process often because there was no effective distribution rules can refer to, lead to a pier laden vehicle waiting distribution caused by congestion. This study by the association rules of data mining methods to retrieve various schedules, content delivery and distribution locations, and thus the formation of the basket, with the relevance of interrelated rules refer to find it. In this study, the use of association rules rule the aggregate distribution is obtained, only that the same timetable and distribution of goods loaded reference rule, if delivery mainland thirds stone, they will delivery six points continent stone; and distribution Hualien sand, it must distribution will Hualien Hualien sixth of stone or stone-thirds. Whereby rules can help dispatchers to quickly make a correct and efficient delivery schedule, another of the study were not included because of the time it is not possible depth information delivery order.
36
Cowen, Nicholas L. "Universal Design Rules from Product Pairs and Association Rule Based Learning". 2010. http://hdl.handle.net/1969.1/ETD-TAMU-2010-05-7964.
Testo completoAbstract (sommario):
A product pair is two products with similar functionality that satisfy the same
high level need but are different by design. The goal of this research is to apply
association rule-based learning to product pairs and develop universal design rules to be
used during the conceptual design phase. The Apriori algorithm produced 1,023
association rules with input parameters of 70% minimum confidence and 0.5%
minimum support levels. These rules were down-selected based on the prescribed rule
format of: (Function, Typical User Activity) ? (Change, Universal User Activity). In
other words, for a given product function and user activity, the rules suggest a design
change and new user activity for a more universal product.
This research presents 29 universal design rules to be used during the conceptual
design stage. These universal design rules suggest a parametric, morphological,
functional, or no design change is needed for a given user activity and product function.
No design change rules confirm our intuition and also prevent inefficient design efforts.
A parametric design change is suggested for actionfunction elements involving find hand
use to manipulate a product. Morphological design changes are proposed to solve actionfunction elements in a slightly more complex manner without adding or
subtracting overall functionality. For example, converting human energy to mechanical
energy with the upper body opposed to the lower body or actuating fluid flow with
motion sensors instead of manual knobs. The majority of the recommended functional
changes involve automating a product to make it more universal which might not be
apparently obvious to designers during conceptual design.
37
Валігура, Марта Вікторівна, e Marta Viktorivna Valihura. "Підвищення ефективності роботи книгарні за рахунок сегментації споживачів". Master's thesis, 2020. http://elartu.tntu.edu.ua/handle/lib/34031.
Testo completoAbstract (sommario):
При виконанні першого розділу роботи проведено дослідження предметної області фірми по продажу товарів, а зокрема інтернет-магазину книгарні, який був аналогом для нашої розробки. Також проведено вивчення основних бізнес-процесів, які проводяться за допомогою інтернет-магазину і вияснено, що відсутність рекомендації книг може понизити продаж книг.
При виконанні другого розділу проведено опис постановки завдання даного дослідження, зокрема знаходження множини книг системи, які будуть запропоновані користувачу, коли будуть враховані його вподобання. Для того, щоб розв’язати дану проблему, запропоновано провести реінжиніринг бізнес-процесів, зокрема використавши функцію рекомендування.
В третьому розділі проведено опис засобів розробки системи. Наведено інструкцію користувача, яка знайомить з створеним інтерфейсом системи, а також представлено структурну схему для технічного забезпечення.During the first section of the work, a study of the subject area of the company for the sale of goods, and in particular the online store of the bookstore, which was an analogue for our development. A study of the main business processes conducted through the online store was also conducted and it was found that the lack of book recommendations can reduce book sales. In the second section, a description of the task of this study, in particular, finding a set of books of the system, which will be offered to the user, when his preferences are taken into account. In order to solve this problem, it is proposed to reengineer business processes, in particular using the recommendation function. The third section describes the system development tools. The user's instruction which acquaints with the created interface of system is resulted, and also the block diagram for technical maintenance is presented.
Вступ ...9 1 Огляд літературних джерел ...11 1.1 Огляд бізнес-процесів ...11 1.2 Огляд предметної області...14 1.3 Функціональна схема структури ...20 1.4 Постановка задачі ...23 1.5 Проектне рішення ...25 1.6 Висновки до першого розділу ...29 2 Огляд моделей та методів...31 2.1 Постановка задачі згідно дослідження ...33 2.2 Огляд математичної моделі ...34 2.3 Огляд методів та алгоритмів існуючих рішень ...37 2.4 Методика технології прийняття рішень для вибору алгоритмів рекомендаційної системи ...41 2.5 Створення алгоритму розв’язання ...52 2.6 Дослідження результатів ефективності даного методу ...58 2.7 Висновки до другого розділу ...60 3 Практична реалізація ...62 3.1 Технічні засоби розробки системи ...66 3.2 Правила для користувача ...71 3.3 Рекомендації щодо технічного забезпечення ...72 4 Охорона праці та безпека в надзвичайних ситуаціях ...73 4.1 Організація праці при виконанні робіт в книгарні, забезпечення нормативних умов праці ...73 4.2 Використання комп’ютерної техніки для оцінки можливої обстановки...77 4.3 Створення і функціонування системи моніторингу довкілля з метою інтеграції екологічних інформаційних систем, що охоплюють певні території .... 80 4.4 Висновки до четвертого розділу...81 Висновки ...82 Перелік літературних джерел ...83 Додатки
38
Chen, Jian-Zhung, e 陳建中. "Extended Association Rules". Thesis, 2000. http://ndltd.ncl.edu.tw/handle/41880068824930640508.
Testo completoAbstract (sommario):
碩士國立臺灣大學
資訊管理研究所
89
Recently, data mining has become a popular research topic and one of its popular applications is to find an association rule from database transactions. Association rules identify items that are most often bought along with certain other items by a significant fraction of the customer. For example, we may find “60% of the customers who bought milk also bought bread.” Every rule must satisfy two user specified constraints: one is a measure of statistical significance called support and the other is a measure of goodness of the rule called confidence. The support constraint ensures that the frequency of an itemset is large enough. The confidence measures how well a rule predicts the association between items. In this paper, we consider another problem, i.e. what items a customer is likely to buy given that he/she buys a certain set of items and doesn’t buy another set of items. We call these types of rules as the extended association rules . An example of such a rule is “90% of the customers who bought milk, but not buying bread bought hotdogs.” Such an extended association rule can provide valuable information about customer buying patterns and help managers devising better marketing strategies and market segmentation. For example, we may find “80% of the customers who bought coal also bought meat.” Then we can assume that most of the customers buy meat for a barbecue. If we want to know what kind of food a vegetarian would buy for a barbecue, a traditional association rule is useless. Now, we can find an extended association rule “85% of the customers who bought coal but not buying meat bought mushrooms” and we can find that a vegetarian buys mushrooms for a barbecue. Therefore, we can focus on the market segment (vegetarian) and adjust our marketing strategies. Furthermore, the time complexity for mining extended association rules is very high. We design a procedure to reduce the time complexity and present two algorithms: level-wise algorithm and partition algorithm. The level-wise algorithm scans database j+2 times (while the large frequent itemset is a j-itemset), and the partition algorithm scans database twice. According to our experimental results, we found that the partition algorithm always outperformed the level-wise algorithm. Besides, the average size of the maximal potentially frequent itemsets and the parameters of the extended association rules(τ、τ-) have significant effect on the execution time for these two algorithms.
39
Shaikh, Mateen. "Topics in Association Rules". Thesis, 2013. http://hdl.handle.net/10214/7250.
Testo completoAbstract (sommario):
Association rules are a useful concept in data mining with the goal of summa- rizing the strong patterns that exist in data. We have identified several issues in mining association rules and addressed them in three main areas. The first area we explore is standardized interestingness measures. Different interestingness measures exist on different ranges, and interpreting them can be subtly problematic. We standardize several interestingness measures and show how these are useful to consider in association rule mining in three examples. A second area we address is incomplete transactions. By applying statistical methods in new ways to association rules, we provide a more comprehensive means of analyzing incomplete transactions. We also describe how to find families of distributions for interestingness measure values when transactions are incomplete. Finally, we address the common result of mining: a plethora of association rules. Unlike methods which attempt to reduce the number of resulting rules, we harness this large quantity to find a higher-level set of patterns.NSERC Discovery Grant and OMRI Early Researcher Award
40
Chen, Wei-Ren, e 陳威任. "Mining Utility Association Rules". Thesis, 2015. http://ndltd.ncl.edu.tw/handle/04865121871313091524.
Testo completoAbstract (sommario):
碩士銘傳大學
資訊工程學系碩士班
103
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.
41
Li, Bo-Ying, e 李勃穎. "Predictive association rules algorithm". Thesis, 2010. http://ndltd.ncl.edu.tw/handle/44401111369266655228.
Testo completoAbstract (sommario):
碩士中國文化大學
資訊管理學系
99
In past decades, the association rules technology has be applied in data mining domain. The association rules algorithm has two parts. The first part is finding the frequent item set where purchase of times over support threshold from transaction data. The second part is finding the association rules from frequent item set. In the association rules algorithm, the first part is time-consuming. Apriori algorithm is the most often used association rules algorithm in former association rules algorithm. Although, this algorithm can finding the frequent item set. But it has two shortcomings. The first shortcoming is generating candidate item set too much. The second shortcoming is scanning transaction data times without number. Therefore give occasion to time-consuming. Many experts propose the improvement ways in view of these two shortcomings. However the improvement ways are still using Apriori algorithm structure. In this paper we propose predictive association rules algorithm. This algorithm can finding the frequent item set quickly. Predictive association rules algorithm only need scan the database two times. First scan finish length-Item distribute total table. Then using the length error number and frequent error number predictive all frequent item set. Finally scan the transaction finding real frequent item set.
42
Su, Wei-Tu, e 蘇威圖. "Mining Multidimensional Intertransaction Association Rules". Thesis, 2002. http://ndltd.ncl.edu.tw/handle/54923326716084839179.
Testo completoAbstract (sommario):
碩士國立臺灣大學
資訊管理研究所
90
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.
43
Huang, Bingfong, e 黃炳逢. "Association Rules for Page Layout". Thesis, 2004. http://ndltd.ncl.edu.tw/handle/37480762036318049316.
Testo completoAbstract (sommario):
碩士國立臺北科技大學
工業工程與管理研究所
92
Association Rule Mining aims to discover the customers’ buying patterns in order to assist in product layout decisions and sales promotion decisions. Traditional association rules consider only the conjunctive associations among items. In practice, other associations among items, such as disjunctive associations, may be very useful for decision making. In this thesis, we consider the use of the generalized association rules for page layout. We show that the generalized association rules can be very useful for designing the commercial websites and performing one-to-one marketing. An example system that uses the generalized association rules for courses recommendations for students is developed and reported.
44
Li, Li-Ya, e 李立雅. "Inter-sequence Association Rules Mining". Thesis, 2003. http://ndltd.ncl.edu.tw/handle/07341599579887641679.
Testo completoAbstract (sommario):
碩士國立臺灣大學
資訊管理研究所
91
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.
45
Chang, Paul C. M., e 張仲銘. "Mining Association Rules by Sorts". Thesis, 1998. http://ndltd.ncl.edu.tw/handle/27186430188696978772.
Testo completoAbstract (sommario):
碩士國立清華大學
資訊工程學系
86
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.
46
Rocha, Sara Filipa Gonçalves. "Drug Repurposing using Association Rules". Master's thesis, 2021. https://hdl.handle.net/10216/139188.
Testo completo
47
Lee, Yi-Feng, e 李藝鋒. "A improvement of Decision Tree using Association Rule Algorithms and Genetic Algorithms in Small Disjunct rules". Thesis, 2005. http://ndltd.ncl.edu.tw/handle/08215693292176255752.
Testo completoAbstract (sommario):
碩士長庚大學
資訊管理研究所
93
The decision tree algorithm is used for classification in the field of data mining, but constructing optimal decision trees is an NP-complete problem. For this reason, some scholars use genetic algorithms (GAs) to solve this problem. However, the efficiency of GAs is not good enough so that some scholars use association rule to help not only constructing a better decision tree but improving the efficiency of GAs. On the other hand, some of the previous researches take the entire decision tree to proceed GAs generation. However, the accuracy of big disjuncts with GAs has limited improvement, hence some scholars use small disjuncts which produce shorter rules instead of the big one to improve the efficiency of GAs. Therefore, we want to construct a more accurate decision tree by using association rule mining and GAs in Small Disjunct and then propose a new decision tree algorithm. HAEGT, which can not only get classification rules with highly accurate but highly interpretability. The accuracy and interpretability of our algorithm are demonstrated through some experiment results with the UCI Machine Learning Repository. Besides, our algorithm can raise the efficiency of genetic algorithms in decision tree about 20% and can construct highly interpretability rules.
48
Yi-Ling, Chen. "Mining Spatial Association Rules in Image". 2005. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0001-0907200516580400.
Testo completo
49
Wang, Mei-Hwa, e 王美華. "Discovery of Adaptive-Support Association Rules". Thesis, 2003. http://ndltd.ncl.edu.tw/handle/50965377215664397507.
Testo completoAbstract (sommario):
碩士義守大學
資訊工程學系
91
The objective of this research is to study how to improve the efficiency of the discovery of Adaptive-Support Association Rules (ASAR) for collaborative recommendation systems. Collaborative recommendation (sometimes known as collaborative filtering) is a process by which information on the preferences and actions of a group of users is tracked by a system which then, based on the patterns it observes, tries to make useful recommendations to individual users. Many data mining techniques have recently been proposed for the construction of collaborative recommendation systems, in particular, the fixed step-size adjustment adaptive-support association rule algorithm in [7]. In this work, we propose two adjustable step-size data-mining algorithms to discover the adaptive-support association rules from transaction databases, namely Bisection-based ASAR algorithm and Secant-based ASAR algorithm. Experimental comparisons with the fixed step-size adjustment approach show that our proposed techniques require less computation, both running time and iteration steps, and will always find a corresponding minimum support for the association rules.
50
Chen, Si-Wei, e 陳思偉. "Quantitative Association Rules in Transaction Database". Thesis, 2002. http://ndltd.ncl.edu.tw/handle/93572361803151634072.
Testo completoAbstract (sommario):
碩士輔仁大學
資訊工程學系
90
Mining quantitative association rules is to find the items associated with their quantities, which are purchased frequently and the relationship among them from a large transaction database. From such rules, we focus on which items associated with which quantities are purchased by “most” of the customers. Before generating quantitative association rules, we need to specify a criterion to claim what the "most" is. If the frequency of an itemset associated with quantities purchased satisfies the criterion, then we can say that the items in the itemset are purchased together frequently. If we do not consider the quantities associated with items, then we can specify the criterion definitely to find out association rules. However, for quantitative association rules, the items with different quantities are regarded as different new items, such that it is difficult to satisfy the “most” criterion for those new items. In the previous approaches, they relaxed the criterion in order to find the quantitative association rules. However, we do not know how to relax the criterion. If it is relaxed too much, then there may be many unuseful rules found. If it is relaxed not enough, then there may be no or few rules found. Besides, the range of the quantity associated with an item need to be partitioned into intervals, and some intervals can be combined. The process of partition and combination may loss some information. In this paper, we propose a new approach to discover the quantitative association rules. Our algorithm can overcome the problem of user-specified criterion, the range partition and the interval combination, such that we can find the quantitative association rules in which the users are actually interested.