Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/24800
Title: Intelligent techniques for temporal sequence pattern mining using association rules
Researcher: Krishnamurthy, M
Guide(s): Kannan, A
Keywords: Information and Communication Engineering
Association rule mining
Data mining
Information and communication engineering
Temporal frequent pattern tree
Temporal pattern mining
User preference database
Upload Date: 9-Sep-2014
University: Anna University
Completed Date: 01/04/2012
Abstract: In todays internet era a vast amount of knowledge is stored in the web and database Due to the availability of huge knowledge repositories getting the relevant information is a challenging task and hence it must be mined and extracted Association Rule Mining is one approach for extracting useful knowledge from datasets or database which includes frequent patterns and association rules between the items or attributes of a dataset with varying levels of strength In this thesis an intelligent architecture for effective Frequent Itemset Mining and Association Rule Mining has been proposed and implemented in order to provide effective mining for both temporal database and conventional database The proposed architecture consists of six different components namely User interface Data Set Rule Base Decision Manager Frequent Itemset Generator component and User Profile Manager model which uses the User Preference Database for fixing the support threshold The Frequent Itemset Generator provides frequent itemsets using the standard data structure component Temporal Frequent Pattern Tree algorithm and Hashing with Quadratic Probing for effective data mining These mining methods are used to find frequent itemsets from temporal database as well as conventional transaction database respectively
Pagination: xxi, 188p.
URI: http://hdl.handle.net/10603/24800
Appears in Departments:Faculty of Information and Communication Engineering

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01_title.pdfAttached File87.27 kBAdobe PDFView/Open
02_certificates.pdf2.75 MBAdobe PDFView/Open
03_abstract.pdf8.69 kBAdobe PDFView/Open
04_acknowledgement.pdf6.05 kBAdobe PDFView/Open
05_contents.pdf40.06 kBAdobe PDFView/Open
06_chapter1.pdf36.36 kBAdobe PDFView/Open
07_chapter2.pdf79.23 kBAdobe PDFView/Open
08_chapter3.pdf202.69 kBAdobe PDFView/Open
09_chapter4.pdf2.39 MBAdobe PDFView/Open
10_chapter5.pdf211.99 kBAdobe PDFView/Open
11_chapter6.pdf1.05 MBAdobe PDFView/Open
12_chapter7.pdf10.46 kBAdobe PDFView/Open
13_references.pdf53.44 kBAdobe PDFView/Open
14_publications.pdf6.42 kBAdobe PDFView/Open
15_vitae.pdf5.28 kBAdobe PDFView/Open


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