Please use this identifier to cite or link to this item:
標題: Discovering frequent itemsets by support approximation and itemset clustering
作者: Jea, K.F.
Chang, M.Y.
關鍵字: support approximation;clustering;data mining;combinatorial;approximation;frequent itemset;association rules;efficient;classification;algorithm
Project: Data & Knowledge Engineering
期刊/報告no:: Data & Knowledge Engineering, Volume 65, Issue 1, Page(s) 90-107.
To speed up the task of association rule mining, a novel concept based on support approximation has been previously proposed for generating frequent itemsets. However, the mining technique utilized by this concept may incur unstable accuracy due to approximation error. To overcome this drawback, in this paper we combine a new clustering method with support approximation, and propose a mining method, namely CAC, to discover frequent itemsets based on the Principle of Inclusion and Exclusion. The clustering technique groups highly similar members to improve the accuracy of support approximation. The hit ratio analysis and experimental results presented in this paper verify that CAC improves accuracy. Without repeatedly scanning a database and storing vast information in memory, the CAC method is able mine frequent itemsets with relative stability. The advantages that the CAC method enjoys in both accuracy and performance make it an effective and useful technique for discovering frequent itemsets in a database. (c) 2007 Elsevier B.V. All rights reserved.
ISSN: 0169-023X
DOI: 10.1016/j.datak.2007.10.003
Appears in Collections:資訊科學與工程學系所

Show full item record

Google ScholarTM




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