Fast parallel association rule mining without candidacy generation

被引：69

作者：

Zaïane, OR ^{[1
]}

El-Hajj, M ^{[1
]}

Lu, P ^{[1
]}

机构：

[1] Univ Alberta, Edmonton, AB T6G 2M7, Canada

来源：

2001 IEEE INTERNATIONAL CONFERENCE ON DATA MINING, PROCEEDINGS | 2001年

关键词：

D O I：

10.1109/ICDM.2001.989600

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper we introduce a new parallel algorithm MLFPT (Multiple Local Frequent Pattern Tree) [11] for parallel mining of frequent patterns, based on FP-growth mining, that uses only, two full I/O scans of the database, eliminating the need for generating the candidate items, and distributing the work fairly among processors. We have devised partitioning strategies at different stages of the mining process to achieve near optimal balancing between processors. We have successfully tested our algorithm on datasets larger than 50 million transactions.

引用

页码：665 / 668

页数：4

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