Efficient Mining of Maximal Frequent Itemsets Based on M-Step Lookahead

被引：0

作者：

Meyer, Elijah L. ^{[1
]}

Chung, Soon M. ^{[1
]}

机构：

[1] Wright State Univ, Dept Comp Sci & Engn, Dayton, OH 45435 USA

来源：

PROCEEDINGS OF 2018 5TH INTERNATIONAL CONFERENCE ON DATA AND SOFTWARE ENGINEERING (ICODSE) | 2018年

关键词：

frequent itemset mining; maximal frequent itemsets; Max-Miner; performance analysis;

D O I：

暂无

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

We propose a new maximal frequent itemset mining algorithm, named m-step lookahead. This is a variant of the Max-Miner algorithm that, instead of counting the support of the largest possible superset of each candidate itemset, counts the support of a superset with a predetermined length. This is designed to circumvent the weakness in the Max-Miner algorithm that the probability of finding a frequent superset is extremely low for the first several passes. By looking for a smaller superset, m-step lookahead may find long frequent patterns more quickly than Max-Miner. Our experimental results demonstrate that this is the case for certain datasets and user-defined parameters.

引用

页数：5

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