A Hybrid Approach for Mining Frequent Itemsets

被引：12

作者：

Bay Vo ^{[1
]}

Tuong Le ^{[2
]}

Coenen, Frans ^{[3
]}

Hong, Tzung-Pei ^{[4
]}

机构：

[1] Ton Duc Thang Univ, Ho Chi Minh City, Vietnam

[2] Univ Food Ind, Ho Chih Minh, Vietnam

[3] Univ Liverpool, Dept Comp Sci, Liverpool, Merseyside, England

[4] Natl Univ Kaohsiung, Dept Comp Sci & Informat Engn, Kaohsiung, Taiwan

来源：

2013 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC 2013) | 2013年

关键词：

frequent itemset; PPC-tree; N-list; data mining; BITTABLEFI; ALGORITHMS;

D O I：

10.1109/SMC.2013.791

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Frequent itemset mining is a fundamental element with respect to many data mining problems. Recently, the PrePost algorithm has been proposed, a new algorithm for mining frequent itemsets based on the idea of N-lists. PrePost in most cases outperforms other current state-of-the-art algorithms. In this paper, we present an improved version of PrePost that uses a hash table to enhance the process of creating the N-lists associated with 1-itemsets and an improved N-list intersection algorithm. Furthermore, two new theorems are proposed for determining the "subsume index" of frequent 1-itemsets based on the N-list concept. The experimental results show that the performance of the proposed algorithm improves on that of PrePost.

引用

页码：4647 / 4651

页数：5

共 12 条

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