A parallel Apriori algorithm for frequent itemsets mining

被引：0

作者：

Ye, Yanbin ^{[1
]}

Chiang, Chia-Chu ^{[2
]}

机构：

[1] Acxiom Corp, 1001 Technol Dr, Little Rock, AR 72223 USA

[2] Univ Arkansas, Dept Comp Sci, Little Rock, AR 72204 USA

来源：

FOURTH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING RESEARCH, MANAGEMENT AND APPLICATIONS, PROCEEDINGS | 2006年

关键词：

Apriori; association rules; data mining; frequent itemsets mining (FIM); parallel computing;

D O I：

暂无

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

Finding frequent itemsets is one of the most investigated fields of data mining. The Apriori algorithm is the most established algorithm for frequent itemsets mining (FIM). Several implementations of the Apriori algorithm have been reported and evaluated One of the implementations optimizing the data structure with a trie by Bodon catches our attention. The results of the Bodon's implementation for finding frequent itemsets appear to be faster than the ones by Borgelt and Goethals. In this paper, we revised Bodon's implementation into a parallel one where input transactions are read by a parallel computer. The effect a parallel computer on this modified implementation is presented.

引用

页码：87 / +

页数：2

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