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Dissertations / Theses on the topic 'Association rules mining'

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Published: 4 June 2021

Last updated: 25 July 2025

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1

Cai, Chun Hing. "Mining association rules with weighted items." Hong Kong : Chinese University of Hong Kong, 1998. http://www.cse.cuhk.edu.hk/%7Ekdd/assoc%5Frule/thesis%5Fchcai.pdf.

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Thesis (M. Phil.)--Chinese University of Hong Kong, 1998.<br>Description based on contents viewed Mar. 13, 2007; title from title screen. Includes bibliographical references (p. 99-103). Also available in print.

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Zhou, Zequn. "Maintaining incremental data mining association rules." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/MQ62311.pdf.

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Goulbourne, Graham. "Tree algorithms for mining association rules." Thesis, University of Liverpool, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.250218.

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With the increasing reliability of digital communication, the falling cost of hardware and increased computational power, the gathering and storage of data has become easier than at any other time in history. Commercial and public agencies are able to hold extensive records about all aspects of their operations. Witness the proliferation of point of sale (POS) transaction recording within retailing, digital storage of census data and computerized hospital records. Whilst the gathering of such data has uses in terms of answering specific queries and allowing visulisation of certain trends the v

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Koh, Yun Sing, and n/a. "Generating sporadic association rules." University of Otago. Department of Computer Science, 2007. http://adt.otago.ac.nz./public/adt-NZDU20070711.115758.

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Association rule mining is an essential part of data mining, which tries to discover associations, relationships, or correlations among sets of items. As it was initially proposed for market basket analysis, most of the previous research focuses on generating frequent patterns. This thesis focuses on finding infrequent patterns, which we call sporadic rules. They represent rare itemsets that are scattered sporadically throughout the database but with high confidence of occurring together. As sporadic rules have low support the minabssup (minimum absolute support) measure was proposed to filter

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Pray, Keith A. "Apriori Sets And Sequences: Mining Association Rules from Time Sequence Attributes." Link to electronic thesis, 2004. http://www.wpi.edu/Pubs/ETD/Available/etd-0506104-150831/.

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Thesis (M.S.) -- Worcester Polytechnic Institute.<br>Keywords: mining complex data; temporal association rules; computer system performance; stock market analysis; sleep disorder data. Includes bibliographical references (p. 79-85).

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王漣 and Lian Wang. "A study on quantitative association rules." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1999. http://hub.hku.hk/bib/B31223588.

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Wang, Lian. "A study on quantitative association rules /." Hong Kong : University of Hong Kong, 1999. http://sunzi.lib.hku.hk/hkuto/record.jsp?B2118561X.

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Zhu, Hua. "On-line analytical mining of association rules." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ37678.pdf.

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9

Wu, Jingtong. "Interpretation of association rules with multi-tier granule mining." Thesis, Queensland University of Technology, 2014. https://eprints.qut.edu.au/71455/1/Jing_Wu_Thesis.pdf.

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This study was a step forward to improve the performance for discovering useful knowledge – especially, association rules in this study – in databases. The thesis proposed an approach to use granules instead of patterns to represent knowledge implicitly contained in relational databases; and multi-tier structure to interpret association rules in terms of granules. Association mappings were proposed for the construction of multi-tier structure. With these tools, association rules can be quickly assessed and meaningless association rules can be justified according to the association mappings. Th

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Delpisheh, Elnaz, and University of Lethbridge Faculty of Arts and Science. "Two new approaches to evaluate association rules." Thesis, Lethbridge, Alta. : University of Lethbridge, Dept. of Mathematics and Computer Science, c2010, 2010. http://hdl.handle.net/10133/2530.

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Data mining aims to discover interesting and unknown patterns in large-volume data. Association rule mining is one of the major data mining tasks, which attempts to find inherent relationships among data items in an application domain, such as supermarket basket analysis. An essential post-process in an association rule mining task is the evaluation of association rules by measures for their interestingness. Different interestingness measures have been proposed and studied. Given an association rule mining task, measures are assessed against a set of user-specified properties. However, in prac

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魯建江 and Kin-kong Loo. "Efficient mining of association rules using conjectural information." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2001. http://hub.hku.hk/bib/B31224878.

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Loo, Kin-kong. "Efficient mining of association rules using conjectural information." Hong Kong : University of Hong Kong, 2001. http://sunzi.lib.hku.hk/hkuto/record.jsp?B22505544.

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Palanisamy, Senthil Kumar. "Association rule based classification." Link to electronic thesis, 2006. http://www.wpi.edu/Pubs/ETD/Available/etd-050306-131517/.

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Thesis (M.S.)--Worcester Polytechnic Institute.<br>Keywords: Itemset Pruning, Association Rules, Adaptive Minimal Support, Associative Classification, Classification. Includes bibliographical references (p.70-74).

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李守敦 and 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.

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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.

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Yang, Yuping. "Theory and mining of association rules over large databases /." The Ohio State University, 2000. http://rave.ohiolink.edu/etdc/view?acc_num=osu1488194825668668.

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Zhang, Hongen. "Mining and visualization of association rules over relational DBMSs." [Florida] : State University System of Florida, 2000. http://etd.fcla.edu/etd/uf/2000/ana7033/Etd.pdf.

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Thesis (M.S.)--University of Florida, 2000.<br>Title from first page of PDF file. Document formatted into pages; contains xiii, 100 p.; also contains graphics. Vita. Includes bibliographical references (p. 97-99).

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Chen, Wei-Ren, and 陳威任. "Mining Utility Association Rules." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/04865121871313091524.

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碩士<br>銘傳大學<br>資訊工程學系碩士班<br>103<br>Mining Association Rules can find which products would be purchased by the customer when a customer has bought some products, and we can use association rules to recommend products for customers. Mining High Utility Itemset is to find the combinations of products which could bring high profit to us. However, High Utility Itemset only tells us which products bring high profit but not increase profit when we recommend other product to customer. Therefore, we propose definitions and algorithm of Mining Utility Association Rules to find which product to recommend

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Li, Li-Ya, and 李立雅. "Inter-sequence Association Rules Mining." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/07341599579887641679.

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碩士<br>國立臺灣大學<br>資訊管理研究所<br>91<br>There are many algorithms proposed to find sequential patterns in sequence databases where each transaction contains one sequence. Previously proposed algorithms treat each sequence as an independent one. This kind of mining belongs to intra-transaction sequential patterns mining. In this paper, we propose an algorithm, ProbSif, to mine inter-sequence association rules. Our proposed algorithm consists of three phases. First, we find all large intra-sequence patterns. For each large pattern found, all the time points at which the pattern occurs are re

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Chang, Paul C. M., and 張仲銘. "Mining Association Rules by Sorts." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/27186430188696978772.

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碩士<br>國立清華大學<br>資訊工程學系<br>86<br>In this thesis, we use the knowledge about the sorts of items and transactions to discover association rules among items in a market transaction database. It is natural to divide items into sorts: milk and bread belong to the sort of food while gloves and hats pertain to the sort of clothing. We sort each transaction according to the sorts of items contained by this transaction. Then each sort of transactions will form a subset of the entire database. To disc

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Su, Wei-Tu, and 蘇威圖. "Mining Multidimensional Intertransaction Association Rules." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/54923326716084839179.

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碩士<br>國立臺灣大學<br>資訊管理研究所<br>90<br>Traditionally, association rule data mining almost focuses on finding the associations among items within the same transaction. In this thesis, we explore “Multidimensional Intertrnasaction Association Rules”, which tries to find the association rule from different transactions and extend to multidimensional space. We propose the E-Partition algorithm and use the Grid File as our data structure to find the large itemsets in the database. Besides, we propose the E-DELTA algorithm to deal with the incremental data mining. The experiment shows that the

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Chien, Peng Wang, and 王建鵬. "Find the General Rule of Data Mining Association Rules." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/08735074145658888662.

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碩士<br>萬能科技大學<br>資訊管理研究所<br>99<br>At present the application of association rule mining and research, to exchange products generated discussion targeted mostly clustered, and in the exploration process and output that, there is no a general rule of representation, usually in a unique way or the text description . This study proposes a concept of transactions by participants in the association rule mining as an object. For association rule mining applications more flexible, to entities associated with the set methodology for the extension of a graphical representation, so that regardless of the

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Yang, Chian-Yi, and 楊千儀. "Mining High Utility Quantitative Association Rules." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/2jdtaf.

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碩士<br>銘傳大學<br>資訊傳播工程學系碩士班<br>94<br>Mining weighted association rules consider the importance of items in a large transaction database. Mining quantitative association rules find most quantitative itemsets , which are purchased frequently, and relate with them from a large transaction database. However, weighted association rules didn’t consider the items which their quantities, and quantitative association rules didn’t consider the items which their weighted. Economics mention influence that quantities affect the cost; and high prices are not necessarily to make a profit, that proves, if only

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Yi-Ling, Chen. "Mining Spatial Association Rules in Image." 2005. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0001-0907200516580400.

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Su, Po-Ta, and 蘇伯達. "Mining Association Rules by Ant System." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/51532702116386182507.

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碩士<br>國立清華大學<br>工業工程與工程管理學系<br>90<br>Mining association rules is to find relations among large amount of data so that the pattern of the dataset can be discovered. Many companies use association rules to find the relations among different items to improve their service quality of customers or enlarge their marketplace. Recently, many algorithms have been developed that only consider either non-quantitative data or quantitative data. However, in reality, most data we collected are mixed in types. Since Ant System allows to consider both of data types and has advantages of being efficient in fi

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"Mining association rules with weighted items." 1998. http://library.cuhk.edu.hk/record=b5889513.

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by Cai, Chun Hing.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 1998.<br>Includes bibliographical references (leaves 109-114).<br>Abstract also in Chinese.<br>Acknowledgments --- p.ii<br>Abstract --- p.iii<br>Chapter 1 --- Introduction --- p.1<br>Chapter 1.1 --- Main Categories in Data Mining --- p.1<br>Chapter 1.2 --- Motivation --- p.3<br>Chapter 1.3 --- Problem Definition --- p.4<br>Chapter 1.4 --- Experimental Setup --- p.5<br>Chapter 1.5 --- Outline of the thesis --- p.6<br>Chapter 2 --- Literature Survey on Data Mining --- p.8<br>Chapter 2.1 --- Statistical Approach ---

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Chen, Yi-Ling, and 陳奕伶. "Mining Spatial Association Rules in Image." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/39410529091384817730.

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碩士<br>國立臺灣大學<br>電機工程學研究所<br>93<br>In this paper, we integrate data mining with image processing for discovering spatial relationships in images. We present an image mining framework, Spatial Association Rulemining (SAR), to mine spatial associations located in specific locations of images. A rule in the SAR refers to the occurrences of image content in a pair of spatial locations. The proposed approach is applied to mine color spatial association rules (color-SAR) in landscape scene images so as to demonstrate that the spatial association rules is able to the application of image classificatio

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Huang, Minghua, and 黃明華. "Algorithms for Parallel Association Rules Mining." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/88224762660139352543.

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碩士<br>國立臺灣科技大學<br>管理研究所資訊管理學程<br>87<br>Mining association rules is an important task. Many parallel algorithms have been proposed to expedite the execution of the mining process. In this thesis, we propose a parallel algorithm called ''PBSM'' for shared-disk environments, and implement the PBSM algorithm on an nCUBE parallel computer. In the PBSM algorithm, mining process is divided into two steps. In the first step, multiple processors are used to generate frequent itemsets. Then, in the second phase, a chosen processor is used to generate the related association rules.

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Zhen, Hao, and 振昊. "DPARM: Differential Privacy Association Rules Mining." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/xqj7yw.

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碩士<br>國立臺灣大學<br>電機工程學研究所<br>107<br>In contemporary society, the rapid expansion of data volume has driven the development of data analysis techniques, which makes decision automation possible. Association analysis is an important task in data analysis. The goal is to find all co-occurrence relationships from the transactional dataset, i.e. frequent itemsets or confident association rules. An association rule consists of two parts, the antecedent and the consequent, which means that if the antecedent occurs then the consequent is also possible to happen. Confident association rules are those as

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Wu, Chieh-Ming, and 吳界明. "Data mining for generalized association rules and privacy preservingData mining for generalized association rules and privacy preserving." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/85543535661382633122.

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博士<br>國立雲林科技大學<br>工程科技研究所博士班<br>99<br>Data mining is an analysis method used to extract the unknown and latent information that hides in large dataset which has usable information. In the last few years the data mining model and method have long-term progress and the association rule mining is most often applied. The association rule research focus on discussion how to discover single level association rule effectiveness in the large dataset. In the recent years more and more researchers start to study the problem of multiple level association rules that was advantageous in the knowledge econo

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Wang, Wei-Tse, and 王威澤. "A native XML database association rules mining method and a database compression approach using association rules mining." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/47594741243843634353.

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碩士<br>朝陽科技大學<br>資訊管理系碩士班<br>91<br>With the advancement of technology and popularity of applications in enterprises’ information system, greater and greater amount of data is generated everyday. To properly store and access these data, database applications have come into play and become crucial. The main task of data mining is to help enterprises make their decisions by extracting useful information from the large amount of complicated data storage for reference, so this is why data mining has been recently paid more attention than ever. Also, more storage media for data is required for the in

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LIN, MING-HUNG, and 林銘泓. "Exploringthe Distribution Rules of Aggregate Using Data Mining Association Rule." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/00708958833560184595.

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碩士<br>萬能科技大學<br>資訊管理研究所在職專班<br>104<br>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 i

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Yang, Nai-Hua, and 楊乃樺. "Mining Multidimensional Association Rules for Market Segmentation." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/78f64c.

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碩士<br>銘傳大學<br>資訊管理學系碩士班<br>95<br>Today is a customer-oriented market. Enterprises need to give every customer appropriate service. The more precise information can make accurate and profitable strategies. Association rules provide correlations between data items in large numbers of data. The further exploration is to discover relationship between customer’s features and customer purchasing behaviors. This paper proposes a new method to discover mining multidimensional association rule for market segmentation. We use conditional databases to discover multidimensional association rule, do not sc

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Jen-Feng, Li. "Mining Association Rules in Time-series Databases." 2005. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0001-2507200511203800.

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Ying-Hsiang, Wen. "Parallel Hardware Architecture for Mining Association Rules." 2006. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0001-2507200610192300.

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Wang, Hsing-Kai, and 王星凱. "An Efficient Distributed Association Rules Mining System." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/75735815815407828838.

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碩士<br>淡江大學<br>資訊管理學系<br>91<br>Association rule mining can help the enterprises to capture the consumer behaviors and develop effective marketing strategies. However, the size of transaction database is increasing everyday, how to get timely mining results becomes a serious problem. In this paper, we propose an Effective Distributed Association rule Mining System, EDAMS, to cope with this problem. Unlike other distributed mining systems, a dedicated node is used as data server to collect exchange data among nodes. Thus, the point-to-point broadcasts are avoided and therefore the number of messa

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Li, Jian-Ming, and 李建明. "Mining Quantitative Association Rules in Disease Databases." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/76433798386921713653.

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碩士<br>國立臺灣大學<br>電機工程學研究所<br>88<br>With the computerization of medical information and popularity of medical database, the amount of data grows much more rapidly than ever. There must be numerous known or unknown information hidden behind these data. Traditional statistical approach is not suit for processing such large amount of data. A technique called “Data Mining” is emerging in which the “Association Rules” is the one focusing on the relationship among data items. The technique of mining association rules was first introduced to search the pattern of items that a customer may bu

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Wen, Ying-Hsiang, and 溫英翔. "Parallel Hardware Architecture for Mining Association Rules." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/84140141294459812790.

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碩士<br>國立臺灣大學<br>電機工程學研究所<br>94<br>Generally speaking, to implement Apriori-based association rule mining in hardware, one has to load candidate itemsets and a database into the hardware. Since the capacity of the hardware architecture is fixed, if the number of candidate itemsets or the number of items in the database is larger than the hardware capacity, the items are loaded into the hardware separately. The time complexity is in proportion to the number of candidate itemsets multiplied by the number of items in the database. Too many candidate itemsets and a large database would create a per

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Tung, Chien-Hung, and 董建弘. "Incremental XML Association Rules Mining MiningUsing XQuery." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/55107331051607385174.

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碩士<br>國立高雄第一科技大學<br>資訊管理所<br>94<br>ABSTRACT XML has already been recognized as a standard for electronic data interchange over the Internet. We believe a large amount of data will be represented and stored in XML format in the near future. Therefore, we think it will be indispensable to develop tools for mining information directly from XML data. Although many well-known data minig methods have been developed, they are almost based on relational database formats. In this paper, we will propose an algorithm, called IXARM (Incremental XML Association Rules Mining), which does not only extract as

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Wan-chuen, Lin, and 林琬純. "Mining Association Rules with Multi-dimensional Constraints." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/65164683786356321966.

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碩士<br>國立臺灣大學<br>資訊管理研究所<br>91<br>Association rule mining is an important issue in the area of data mining. Frequent itemset discovery is the key factor in performance of association rule mining. Frequent itemset mining algorithms often generate a very large number of frequent itemsets and rules, which reduce not only the efficiency but also the effectiveness of the mining algorithms since only the subset of the complete frequent itemsets and association rules is of interest to users, and users need additional post-processing to filter through a large number of mined rules to find the useful on

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Han, Hui Ching, and 韓惠靜. "Mining Association Rules Among Time-series Databases." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/66580094049643941505.

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碩士<br>國立臺灣大學<br>資訊管理研究所<br>91<br>While more and more data generated in the form of time-series, there are much more needs to find frequent patterns in time-series data. Time-series data mining becomes more and more popular in recent research areas and has broad applications like analysis of customer purchase patterns, web traversal patterns, etc. Let’s consider the example of stock price fluctuation and trading volume fluctuation patterns of TWSE. There may be some implications that when the stock price went upwards two days before, the stock trading volume may go upwards in followi

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Hsi, Lo Yuan, and 羅元禧. "The Association Rules used on Web Mining." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/20237348252767485110.

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碩士<br>國立臺北大學<br>企業管理學系<br>91<br>With the trend of increasing web population and data, there is existing a common problem that the pages provided on web are not distinguished with others. All of context provided, web structure and navigation path on web are regulated by the designator. The designator had set up the web only in his opinion, most of them can’t make sure if the context on web could fit user’s real demand. This research would discuss how to analyze the data with Data Mining Method. We had dealt with these data in the first step, and then we would find out the relationshi

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郭瑞男. "Mining Quantitative Association Rules with Density Constraint." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/13322880493417591245.

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碩士<br>國立臺灣師範大學<br>資訊教育研究所<br>91<br>A new approach, called PQAR (Partition-based Quantitative Association Rules mining) algorithm, is proposed in this thesis for mining quantitative association rules. This approach finds out all the frequent interval itemsets that satisfy the minimum relative density requirement based on space partitioning method, and the quantitative association rules are produced from these interval itemsets. When mining frequent interval itemsets, PQAR algorithm considers not only the minimum support as the filtering condition, but also the minimum relative density to preven

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Wu, Chiech-Ming, and 吳界明. "Mining Generalized Association Rules Using Pruning Techniques." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/81678867339436425832.

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碩士<br>國立雲林科技大學<br>電子與資訊工程研究所碩士班<br>90<br>The goal of the thesis is to mine generalized association rules using pruning techniques. Given a large transaction database and a hierarchical taxonomy tree of the items, we try to find the association rules between the items at different levels in the taxonomy tree under the assumption that original frequent itemsets and association rules have already been generated beforehand. The primary challenge of designing an efficient mining algorithm is how to make use of the original frequent itemsets and association rules to directly generate new generalized

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Shu-Chen, Chang, and 張淑貞. "Mining Multilevel Association Rules in Large Databases." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/62587213281806320843.

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碩士<br>國立臺灣科技大學<br>資訊管理系<br>89<br>As a useful business intelligence tool, data mining is gaining its popularity in many real life applications. Association rules is an important data mining technique developed for looking for the relationship among items bought by customers in a supermarket. To increase its applicability, some researchers have worked on mining association rules from relational databases. Mining association rules from relational databases is much more difficult than mining association rules from transactional databases, especially when numerical attributes are involved. This pro

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Tseng, Pei Hsi, and 曾佩熙. "A Study of Mining Association Rules with." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/61122980878146152093.

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碩士<br>臺南師範學院<br>資訊教育研究所<br>89<br>In recently years, as the technology innovates and progresses constantly, computers have been utilized in various walks of life. And, because of the rapid progress on Internet and on World Wide Web, it is more convenient for us to get the information. Consequently, in the case of more and more data, a lot of organizations begin to use database systems to store and manage the data they need. As the rapid growth in the size and number of the database, the technology of discovering useful knowledge hidden in the large database has become an important research topi

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Qui, Ding-Ying, and 邱鼎穎. "Mining Weighted Association Rules from Large Database." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/69625187682793463209.

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碩士<br>輔仁大學<br>資訊工程學系<br>89<br>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 improve

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Ling, Chun-Ching, and 凌俊青. "Mining Quantitative Association Rules in Bag Databases." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/64760497556217622481.

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碩士<br>國立中央大學<br>資訊管理研究所<br>87<br>The problem of mining association rules is to find the associations between items in a large database of sales transactions. Although there are a lot of previous researches on this area, a common problem occurred is that the rule only indicates if two items are related but as to in what quantities and in what combinations are missing. Without this information it is impossible to design a competitive combination of sales items since we didn''t know how many units of items should be included. Therefore, if the quantities of items can be included in association

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chien, I.-pei, and 錢依佩. "An Efficient Algorithm for Mining Association Rules." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/94139240649049674349.

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碩士<br>南台科技大學<br>資訊管理系<br>91<br>Due to the improvement of information technologies and popularization of computers, collecting information becomes easier, rapider and more convenient than before. As the time goes by, database cumulates huge and hiding information. Therefore, how to correctly uncover and efficiently mining from those hiding information becomes a very important issue. Hence the technology of data mining becomes one of the solutions. In the technologies of data mining, association rules mining is one of the most popular technology to be used. Association rule mining explores the a

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Lai, Wei Lin, and 賴韋霖. "Improving Apriori Algorithms for Mining Association Rules." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/42956763049200327986.

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碩士<br>南台科技大學<br>資訊管理系<br>97<br>With the development of information technology, enterprises have a lot of way to get information and can use this technology store about a lot of enterprise’s transaction or record in data base. How to find the useful information in database has become the subject which the enterprises pay attention. Association rules technology is generally in data mining. Based on the Internet Technology development and the globalization of business, the transaction database of enterprise is constantly changing all the time, and in order to keep the accuracy of exploring result

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