Thèses sur le sujet « Weighted Association Rule Mining »
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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.
Texte intégralDescription based on contents viewed Mar. 13, 2007; title from title screen. Includes bibliographical references (p. 99-103). Also available in print.
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.
Texte intégralWong, Wai-kit, et 王偉傑. « Security in association rule mining ». Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B39558903.
Texte intégralPalanisamy, Senthil Kumar. « Association rule based classification ». Link to electronic thesis, 2006. http://www.wpi.edu/Pubs/ETD/Available/etd-050306-131517/.
Texte intégralKeywords: Itemset Pruning, Association Rules, Adaptive Minimal Support, Associative Classification, Classification. Includes bibliographical references (p.70-74).
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.
Texte intégralIcev, Aleksandar. « DARM distance-based association rule mining ». Link to electronic thesis, 2003. http://www.wpi.edu/Pubs/ETD/Available/etd-0506103-132405.
Texte intégralHajYasien, Ahmed. « Preserving Privacy in Association Rule Mining ». Thesis, Griffith University, 2007. http://hdl.handle.net/10072/365286.
Texte intégralThesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Information and Communication Technology
Faculty of Engineering and Information Technology
Full Text
Vithal, Kadam Omkar. « Novel applications of Association Rule Mining- Data Stream Mining ». AUT University, 2009. http://hdl.handle.net/10292/826.
Texte intégralLin, Weiyang. « Association rule mining for collaborative recommender systems ». Link to electronic version, 2000. http://www.wpi.edu/Pubs/ETD/Available/etd-0515100-145926.
Texte intégralRantzau, Ralf. « Extended concepts for association rule discovery ». [S.l. : s.n.], 1997. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB8937694.
Texte intégralAhmed, 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.
Texte intégralBogorny, 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.
Texte intégralThe 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.
Shrestha, Anuj. « Association Rule Mining of Biological Field Data Sets ». Thesis, North Dakota State University, 2017. https://hdl.handle.net/10365/28394.
Texte intégralBioinformatics Seed Grant Program NIH/UND
National Science Foundation (NSF) Grant IIA-1355466
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.
Texte intégralMahmood, Qazafi. « LC - an effective classification based association rule mining algorithm ». Thesis, University of Huddersfield, 2014. http://eprints.hud.ac.uk/id/eprint/24274/.
Texte intégralBaez, 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.
Texte intégralFjällström, 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.
Texte intégralPray, 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/.
Texte intégralKeywords: mining complex data; temporal association rules; computer system performance; stock market analysis; sleep disorder data. Includes bibliographical references (p. 79-85).
Delpisheh, Elnaz, et 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.
Texte intégralviii, 85 leaves : ill. ; 29 cm
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.
Texte intégralHahsler, Michael, Kurt Hornik et 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.
Texte intégralSeries: Research Report Series / Department of Statistics and Mathematics
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.
Texte intégralRahman, Sardar Muhammad Monzurur, et 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.
Texte intégralKoukal, 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.
Texte intégralZang, Hao. « Non-redundant sequential association rule mining based on closed sequential patterns ». Thesis, Queensland University of Technology, 2010. https://eprints.qut.edu.au/46166/1/Hao_Zang_Thesis.pdf.
Texte intégralMarinica, Claudia. « Association Rule Interactive Post-processing using Rule Schemas and Ontologies : aripso ». Phd thesis, Nantes, 2010. https://archive.bu.univ-nantes.fr/pollux/show/show?id=90a57cc4-245f-420d-ac2b-f9ad7929e0f7.
Texte intégralThis thesis is concerned with the merging of two active research domains: Knowledge Discovery in Databases - Association Rule Mining technique, and Knowledge Engineering - representation languages of Semantic Web. The usefulness of association rule technique is strongly limited by the huge amount and the low quality of delivered 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, and most of them are based on data structure. However, rule interestingness strongly depends on user knowledge and goals. In this context, it is crucial to help the user with an efficient technique to reduce the number of rules while keeping interesting ones. This work addresses two main issues: the integration of user knowledge in the discovery process and the interactivity with the user. The first issue requires an accurate and flexible formalism to express user knowledge such as ontologies in the Semantic Web. The second one proposes a more iterative mining process allowing the user to explore the rule space incrementally focusing on interesting rules. The main contributions of this work can be summarized as follows: (i) A model to represent user knowledge. First, we propose to represent user domain knowledge by means of ontologies. Second, we develop a new formalism, called "Rule Schema", which allows the user to define his/her expectations throughout ontology concepts. Last, we suggest the user a set of "mining Operators" to be applied over Rule Schemas. (ii) A new post-processing approach, ARJPSO. Lt allows the user to reduce the volume of the discovered rules by keeping only the interesting rules. ARIPSO is an interactive process integrating user knowledge by means of the proposed model. The interactive loop allows at each step the user to change the provided information and to reiterate the post-processing phase. (iii) The implementation in post-processing of ARJPSO. The developed tool is complete and operational, and it implements all the functionalities described in the approach. An alternative implementation, without post-processing, was proposed (ARLIUS). It consists in an interactive local mining process. (iv) An experimental study analyzing the approach efficiency and the discovered rule quality. For this purpose, we used a large real-life database; for ARJPSO, the experimentation was carried out in complete cooperation with the domain expert. From an input set of nearly 400 thousand rules, for different scenarios, ARIPSO filtered between 3 and 200 rules validated by the expert
Yang, Wanzhong. « Granule-based knowledge representation for intra and inter transaction association mining ». Thesis, Queensland University of Technology, 2009. https://eprints.qut.edu.au/30398/1/Wanzhong_Yang_Thesis.pdf.
Texte intégralYang, Wanzhong. « Granule-based knowledge representation for intra and inter transaction association mining ». Queensland University of Technology, 2009. http://eprints.qut.edu.au/30398/.
Texte intégralIsik, 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.
Texte intégralhence, 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.
Smith, David T. « A Formal Concept Analysis Approach to Association Rule Mining : The QuICL Algorithms ». NSUWorks, 2009. http://nsuworks.nova.edu/gscis_etd/309.
Texte intégralAljandal, 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.
Texte intégralWu, Jingtong. « Interpretation of association rules with multi-tier granule mining ». Thesis, Queensland University of Technology, 2014. https://eprints.qut.edu.au/71455/1/Jing_Wu_Thesis.pdf.
Texte intégralLi, Jiuyong. « Optimal and Robust Rule Set Generation ». Thesis, Griffith University, 2002. http://hdl.handle.net/10072/366394.
Texte intégralThesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Computing and Information Technology
Science, Environment, Engineering and Technology
Full Text
Abar, Orhan. « Rule Mining and Sequential Pattern Based Predictive Modeling with EMR Data ». UKnowledge, 2019. https://uknowledge.uky.edu/cs_etds/85.
Texte intégralToprak, Serkan. « Data Mining For Rule Discovery In Relational Databases ». Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/12605356/index.pdf.
Texte intégralMahamaneerat, 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.
Texte intégral周嘉伶. « A Weighted Load-Balancing Parallel Apriori Algorithm for Association Rule Mining ». Thesis, 2008. http://ndltd.ncl.edu.tw/handle/36013298507693766438.
Texte intégral中華大學
資訊管理學系(所)
96
Because of the exponential growth in worldwide information, companies have to deal with an ever growing amount of digital information. One of the most important challenges for data mining is quickly and correctly finding the relationship between data. The Apriori algorithm is the most popular technique in association rules mining; however, when applying this method, a database has to be scanned many times and many candidate itemsets are generated. Parallel computing is an effective strategy for accelerating the mining process. In this thesis, the Weighted Distributed Parallel Apriori algorithm (WDPA) is presented as a solution to this problem. In the proposed method, metadata are stored in TID forms, thus only a single scan to the database is needed. The TID counts are also taken into consideration, and therefore better load-balancing as well as reducing idle time for processors can be achieved. According to the experimental results, WDPA outperforms other algorithms while having lower minimum support or having large database. Moreover, under the some situations, WDPA spends only about 5% of the time that other algorithms in previous works spend.
« Mining association rules with weighted items ». 1998. http://library.cuhk.edu.hk/record=b5889513.
Texte intégralThesis (M.Phil.)--Chinese University of Hong Kong, 1998.
Includes bibliographical references (leaves 109-114).
Abstract also in Chinese.
Acknowledgments --- p.ii
Abstract --- p.iii
Chapter 1 --- Introduction --- p.1
Chapter 1.1 --- Main Categories in Data Mining --- p.1
Chapter 1.2 --- Motivation --- p.3
Chapter 1.3 --- Problem Definition --- p.4
Chapter 1.4 --- Experimental Setup --- p.5
Chapter 1.5 --- Outline of the thesis --- p.6
Chapter 2 --- Literature Survey on Data Mining --- p.8
Chapter 2.1 --- Statistical Approach --- p.8
Chapter 2.1.1 --- Statistical Modeling --- p.9
Chapter 2.1.2 --- Hypothesis testing --- p.10
Chapter 2.1.3 --- Robustness and Outliers --- p.11
Chapter 2.1.4 --- Sampling --- p.12
Chapter 2.1.5 --- Correlation --- p.15
Chapter 2.1.6 --- Quality Control --- p.16
Chapter 2.2 --- Artificial Intelligence Approach --- p.18
Chapter 2.2.1 --- Bayesian Network --- p.19
Chapter 2.2.2 --- Decision Tree Approach --- p.20
Chapter 2.2.3 --- Rough Set Approach --- p.21
Chapter 2.3 --- Database-oriented Approach --- p.23
Chapter 2.3.1 --- Characteristic and Classification Rules --- p.23
Chapter 2.3.2 --- Association Rules --- p.24
Chapter 3 --- Background --- p.27
Chapter 3.1 --- Iterative Procedure: Apriori Gen --- p.27
Chapter 3.1.1 --- Binary association rules --- p.27
Chapter 3.1.2 --- Apriori Gen --- p.29
Chapter 3.1.3 --- Closure Properties --- p.30
Chapter 3.2 --- Introduction of Weights --- p.31
Chapter 3.2.1 --- Motivation --- p.31
Chapter 3.3 --- Summary --- p.32
Chapter 4 --- Mining weighted binary association rules --- p.33
Chapter 4.1 --- Introduction of binary weighted association rules --- p.33
Chapter 4.2 --- Weighted Binary Association Rules --- p.34
Chapter 4.2.1 --- Introduction --- p.34
Chapter 4.2.2 --- Motivation behind weights and counts --- p.36
Chapter 4.2.3 --- K-support bounds --- p.37
Chapter 4.2.4 --- Algorithm for Mining Weighted Association Rules --- p.38
Chapter 4.3 --- Mining Normalized Weighted association rules --- p.43
Chapter 4.3.1 --- Another approach for normalized weighted case --- p.45
Chapter 4.3.2 --- Algorithm for Mining Normalized Weighted Association Rules --- p.46
Chapter 4.4 --- Performance Study --- p.49
Chapter 4.4.1 --- Performance Evaluation on the Synthetic Database --- p.49
Chapter 4.4.2 --- Performance Evaluation on the Real Database --- p.58
Chapter 4.5 --- Discussion --- p.65
Chapter 4.6 --- Summary --- p.66
Chapter 5 --- Mining Fuzzy Weighted Association Rules --- p.67
Chapter 5.1 --- Introduction to the Fuzzy Rules --- p.67
Chapter 5.2 --- Weighted Fuzzy Association Rules --- p.69
Chapter 5.2.1 --- Problem Definition --- p.69
Chapter 5.2.2 --- Introduction of Weights --- p.71
Chapter 5.2.3 --- K-bound --- p.73
Chapter 5.2.4 --- Algorithm for Mining Fuzzy Association Rules for Weighted Items --- p.74
Chapter 5.3 --- Performance Evaluation --- p.77
Chapter 5.3.1 --- Performance of the algorithm --- p.77
Chapter 5.3.2 --- Comparison of unweighted and weighted case --- p.79
Chapter 5.4 --- Note on the implementation details --- p.81
Chapter 5.5 --- Summary --- p.81
Chapter 6 --- Mining weighted association rules with sampling --- p.83
Chapter 6.1 --- Introduction --- p.83
Chapter 6.2 --- Sampling Procedures --- p.84
Chapter 6.2.1 --- Sampling technique --- p.84
Chapter 6.2.2 --- Algorithm for Mining Weighted Association Rules with Sampling --- p.86
Chapter 6.3 --- Performance Study --- p.88
Chapter 6.4 --- Discussion --- p.91
Chapter 6.5 --- Summary --- p.91
Chapter 7 --- Database Maintenance with Quality Control method --- p.92
Chapter 7.1 --- Introduction --- p.92
Chapter 7.1.1 --- Motivation of using the quality control method --- p.93
Chapter 7.2 --- Quality Control Method --- p.94
Chapter 7.2.1 --- Motivation of using Mil. Std. 105D --- p.95
Chapter 7.2.2 --- Military Standard 105D Procedure [12] --- p.95
Chapter 7.3 --- Mapping the Database Maintenance to the Quality Control --- p.96
Chapter 7.3.1 --- Algorithm for Database Maintenance --- p.98
Chapter 7.4 --- Performance Evaluation --- p.102
Chapter 7.5 --- Discussion --- p.104
Chapter 7.6 --- Summary --- p.105
Chapter 8 --- Conclusion and Future Work --- p.106
Chapter 8.1 --- Summary of the Thesis --- p.106
Chapter 8.2 --- Conclusions --- p.107
Chapter 8.3 --- Future Work --- p.108
Bibliography --- p.108
Appendix --- p.115
Chapter A --- Generating a random number --- p.115
Chapter B --- Hypergeometric distribution --- p.116
Chapter C --- Quality control tables --- p.117
Chapter D --- Rules extracted from the database --- p.120
Qui, Ding-Ying, et 邱鼎穎. « Mining Weighted Association Rules from Large Database ». Thesis, 2001. http://ndltd.ncl.edu.tw/handle/69625187682793463209.
Texte intégral輔仁大學
資訊工程學系
89
Mining association rules is to find associations among items from large transaction databases. Weighted association rule is also to describe the associations among items, and the importance of each item is considered. Hence, weighted association rules can provide more information than that of association rules. There are many researchers that have proposed their algorithms for mining association rules. Apriori algorithm needs to scan database many times and cost much search time for mining association rules, which is very inefficient. Many other algorithms improved the efficiency of Apriori algorithm, but a lot of memory space need to be taken. There are few approached proposed for mining weighted association rules. The previous approaches also take a lot of time to scan database and search for the needed information. In this thesis, we propose two new algorithms --- delay transaction disassemble (DTD) and Weighted Daley Transaction Disassembled (WDTD) for mining association rules and weighted association rules, respectively, which is very efficient and need not take much memory space. We observe that most of time spent for scanning the database is to disassemble each transaction in the database. The main idea of DTD and WDTD algorithms are that the transaction is not disassembled until it needs to be used. For mining weighted association rules, we also propose an AVL-index tree to store the transactions and the transaction overlap technique to further reduce the execution time and the memory space. The experimental results show that our algorithms outperform other algorithms for mining association rules and weighted association rules.
Chen, Show-Ju, et 陳秀如. « An efficient association rules mining algorithm and its application in mining weighted quantitative association rules with multiple support threshold ». Thesis, 2004. http://ndltd.ncl.edu.tw/handle/94659598291095856971.
Texte intégral南台科技大學
資訊管理系
92
The generation of frequent itemsets is an essential and time-consuming step in mining association rules. Most of the studies adopt the Apriori-based approach, which has great effort in generating candidate itemsets and needs multiple database accesses. Recent studies indicate like FP-Tree approach has been utilized to avoid the generation of candidate itemsets and scan transaction database only twice, but they work with more complicated data structure. Therefore, algorithms for efficient mining of frequent patterns are in urgent demand. This thesis aims to improve both time and space efficiency in mining frequent itemsets. We propose a novel QSD(Quick Simple Decomposition) algorithm using simple decompose principle which derived from minimal heap tree, we can discover the frequent itemsets quickly under once database scan. Meanwhile, QSD algorithm is not necessary to rescan database and reconstruct data structure when database is updated or minimum support is varied. Moreover, we apply the features of the QSD algorithm to explore profit weight-based quantitative association rules with multiple support threshold in accordance with item’s characteristics. The derived rule can solve the problem that itemset with high profit but few trading times was difficult to find out. Comprehensive experiments have been conducted to assess the performance of the proposed algorithm. Experimental results show that the QSD algorithm outperform previously ones, like ICI, FP-Tree algorithms etc.
Liao, Yuan-Fong, et 廖原豐. « Causal Association Rule Mining ». Thesis, 2006. http://ndltd.ncl.edu.tw/handle/sy5ufc.
Texte intégral國立中央大學
資訊管理研究所
94
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.
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.
Texte intégralTai, Tzu-Chiang, et 戴子強. « Using Weighted Association Rule to Decide Storage Assignment ». Thesis, 2017. http://ndltd.ncl.edu.tw/handle/p5x9r9.
Texte intégral國立交通大學
管理學院工業工程與管理學程
105
The objective of storage assignment in the warehouse management is to reduce the order picking distance and save labor cost. Most literature related to storage assignment policies focus on the class-based policy and EIQ (Order Entry, Item, Quantity) analysis to find the product properties, such as turnover rate and order frequency. The traditional approaches used the analysis of the transaction data, while this paper applies the data mining to find the weighted association rules with the product forecast ship quantity. The weighted association rule emphasizes the relationship of products and turnover rate in future. Finally, the real world case shows that the weighted association rule decreases travel distances by 22%, and the statistical results show that our proposed mechanism can significantly improve the performance of reduce the picking distances.
Lin, Shih Hsiang, et 林士翔. « DARM : Doughnut-shaped Association Rule Mining ». Thesis, 2009. http://ndltd.ncl.edu.tw/handle/54386438560648611106.
Texte intégral長庚大學
資訊管理學研究所
97
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.
Cheng, Yung-Hsiung, et 鄭永雄. « A study of association rule mining algorithms ». Thesis, 2007. http://ndltd.ncl.edu.tw/handle/12205682895999423189.
Texte intégral義守大學
資訊管理學系碩士班
95
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.
Lin, Ming-Yen, et 林明言. « Efficient Algorithms for Association Rule Mining and Sequential Pattern Mining ». Thesis, 2003. http://ndltd.ncl.edu.tw/handle/m8z62p.
Texte intégral國立交通大學
資訊工程系所
92
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.
Jin, Weiqing. « Fuzzy classification based on fuzzy association rule mining ». 2004. http://www.lib.ncsu.edu/theses/available/etd-12072004-130619/unrestricted/etd.pdf.
Texte intégralWu, Chin-Wei, et 吳靜薇. « Association Rule Mining For Enrollment Grade And Graduate ». Thesis, 2013. http://ndltd.ncl.edu.tw/handle/48273824059652510501.
Texte intégral國立高雄師範大學
資訊教育研究所
101
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.
Liu, Po-Ting, et 劉柏廷. « Association Rule Based Relational Mining for Stock Trading ». Thesis, 2007. http://ndltd.ncl.edu.tw/handle/99939684843737594402.
Texte intégral國立中央大學
資訊管理研究所
95
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.
Wu, Wei-Mao, et 吳幃楙. « The Research of Multilevel Association Rule Mining Model ». Thesis, 2014. http://ndltd.ncl.edu.tw/handle/62557825511482111060.
Texte intégral僑光科技大學
資訊科技研究所
102
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.