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Title: Different approaches for miningassociation rules from Multi relational databases
Researcher: Vimal kumar D
Guide(s): Tamilarasi A
Keywords: Genetic algorithm
Mining algorithm
Optimization algorithm
Upload Date: 10-Feb-2015
University: Anna University
Completed Date: 01/10/2014
Abstract: Defining a multi relational pattern that is suitably meaningful and discovering vital information through efficient mining algorithm is a challenging problem and significant works have been presented in the literature to solve this challenging task Accordingly an efficient approach for effectual mining of relational patterns is developed from the multi relational database Primarily the multi relational database is represented using a tree based data structure without changing the relations A tree pattern mining algorithm is designed and applied on the constructed tree based data structure for extracting the multi relational frequent patterns The designed mining algorithm makes use of positional data information from the tree data structure so that the required time for mining algorithm is more efficient and effective Then in the second phase of the research the adaptation of GA is done to optimize the multi relational pattern to rule mining The Genetic algorithm Optimization algorithm together with Mining algorithm helps to mine the most significant interesting rules An optimized rule generation with the newly designed fitness function of genetic algorithm is developed Initially every rule is evaluated based on the fitness function and GA has sorted the evaluated rules based on the fitness values subsequently it selects the rules which are greater than the fitness rate as optimal rules newline
Pagination: xx, 174p.
Appears in Departments:Faculty of Science and Humanities

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01_title.pdfAttached File28.35 kBAdobe PDFView/Open
02_certificate.pdf563.85 kBAdobe PDFView/Open
03_abstract.pdf8.25 kBAdobe PDFView/Open
04_acknowledgement.pdf9.12 kBAdobe PDFView/Open
05_content.pdf33.97 kBAdobe PDFView/Open
06_chapter1.pdf123.02 kBAdobe PDFView/Open
07_chapter2.pdf54.03 kBAdobe PDFView/Open
08_chapter3.pdf549.35 kBAdobe PDFView/Open
09_chapter4.pdf483.36 kBAdobe PDFView/Open
10_chapter5.pdf155.12 kBAdobe PDFView/Open
11_chapter6.pdf16.04 kBAdobe PDFView/Open
12_reference.pdf433.76 kBAdobe PDFView/Open
13_publication.pdf13.2 kBAdobe PDFView/Open

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