Cluster based bit vector mining algorithm for finding frequent itemsets in temporal databases

被引：1

作者：

Krishnamurthy, M. ^{[1
]}

Kannan, A. ^{[3
]}

Baskaran, R. ^{[2
]}

Kavitha, M. ^{[1
]}

机构：

[1] Sri Venkateswara Coll Engn, Dept Comp Applicat, Sriperumbudur 602105, India

[2] Anna Univ, Dept Comp Sci & Engn, Madras 600025, Tamil Nadu, India

[3] Anna Univ, Dept Informat Sci & Technol, Madras 600025, Tamil Nadu, India

来源：

WORLD CONFERENCE ON INFORMATION TECHNOLOGY (WCIT-2010) | 2011年 / 3卷

关键词：

Frequent Itemsets; Association Rule; Clustering; Cluster Table; Bit Vector; Temporal Database; EFFICIENT METHOD; ASSOCIATION;

D O I：

10.1016/j.procs.2010.12.086

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

In this paper, we introduce an efficient algorithm using a new technique to find frequent itemsets from a huge set of itemsets called Cluster based Bit Vectors for Association Rule Mining (CBVAR). In this work, all the items in a transaction are converted into bits (0 or 1). A cluster is created by scanning the database only once. Then frequent 1-itemsets are extracted directly from the cluster table. Moreover, frequent k-itemsets, where k >= 2 are obtained by using Logical AND between the items in a cluster table. This approach reduces main memory requirement since it considers only a small cluster at a time and as scalable for any large size of database. The overall performance of this method is significantly better than that of the previously developed algorithms for effective decision making. (C) 2010 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the Guest Editor.

引用

页数：11

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