Please use this identifier to cite or link to this item: http://localhost:8081/xmlui/handle/123456789/9819
Title: PERFORMANCE COMPARISON- OF MULTILEVEL ASSOCIATION -RULE MINING ALGORITHMS
Authors: Mandowara, Sunil Kumar
Keywords: ELECTRONICS AND COMPUTER ENGINEERING;MULTILEVEL ASSOCIATION -RULE MINING ALGORITHMS;DATA MINING;KNOWLEDGE DISCOVERY IN DATABASE
Issue Date: 2004
Abstract: With the widespread computerization in business, government, and science, the efficient and effective discovery of interesting information from large databases becomes essential. Data mining or Knowledge Discovery in Database (KDD) emerges as a solution to the data analysis problems faced by many organizations. Association rule mining finds interesting association among a large set of data items. Mining of association rules mainly focuses at a single conceptual level. In a large database of transaction, where each transaction consist of a set of items, and a taxonomy (is-a hierarchy) on items, it is required to find out the associations at multiple conceptual levels. Mining association rules at multilevel may lead to the discovery of more specific and concrete knowledge from data. In this dissertation, multilevel level association rule mining algorithms have been evaluated and compared. An algorithm has been extended for the cross level association rule mining which discover the additional strong association rules in taxonomy. All * algorithms have been implemented and tested on Synthetic databases which are generated using a randomized algorithm. The performance indices used for performance comparisons are minimum support threshold at different levels and varying number of transactions. All algorithms are implemented" using JAVA language and are tested on Microsoft Windows XP platform.
URI: http://hdl.handle.net/123456789/9819
Other Identifiers: M.Tech
Research Supervisor/ Guide: Singh, Kuldip
Gupta, Sumit
metadata.dc.type: M.Tech Dessertation
Appears in Collections:MASTERS' DISSERTATIONS (E & C)

Files in This Item:
File Description SizeFormat 
ECDG11594.pdf4.11 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.