Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/39196
Title: Association rule mining using directed graphs and hypergraphs
Researcher: Sabeen S
Guide(s): Arumugam S
Keywords: Data mining
Divide and conquer and a greedy
Upload Date: 20-Apr-2015
University: Anna University
Completed Date: 01/12/2014
Abstract: Data mining is a potential tool for prolific analysis of data The task of association rule mining in a large database is one of the important functionalities in data mining Mining association rules from large databases play an essential role in many data mining tasks and have broad applications Most of the previously proposed methods adopt Apriori like candidate generation and test approaches and FP growth divide and conquer approach to decompose both the mining task and the databases However these methods may encounter serious challenges when mining frequent patterns for generating association rules newlineIn this work we develop a directed graph model along with a path system for representing a large transaction database This model is used for mining frequent patterns from large databases of transactions and for generating strong association rules Directed graph model adopts a divide and conquer and a greedy approach to decompose the mining task and the database Then path system in directed graphs is used to avoid the costly candidate generation and test processing completely Moreover, effective data structures are proposed to compress crucial information about frequent patterns and avoid expensive repeated database scan newline newline
Pagination: xxii, 154p.
URI: http://hdl.handle.net/10603/39196
Appears in Departments:Faculty of Science and Humanities

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02_certificate.pdf366.62 kBAdobe PDFView/Open
03_abstract.pdf7.39 kBAdobe PDFView/Open
04_acknowledgement.pdf6.12 kBAdobe PDFView/Open
05_content.pdf46.62 kBAdobe PDFView/Open
06_chapter1.pdf223.58 kBAdobe PDFView/Open
07_chapter2.pdf728.19 kBAdobe PDFView/Open
08_chapter3.pdf726.07 kBAdobe PDFView/Open
09_chapter4.pdf265.81 kBAdobe PDFView/Open
10_chapter5.pdf714.16 kBAdobe PDFView/Open
11_reference.pdf147.35 kBAdobe PDFView/Open
12_publication.pdf28.49 kBAdobe PDFView/Open
13_vitae.pdf7.25 kBAdobe PDFView/Open


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