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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 11/2019

04-01-2019 | Original Article

HashEclat: an efficient frequent itemset algorithm

Authors: Chunkai Zhang, Panbo Tian, Xudong Zhang, Qing Liao, Zoe L. Jiang, Xuan Wang

Published in: International Journal of Machine Learning and Cybernetics | Issue 11/2019

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Abstract

The Eclat algorithm is one of the most widely used frequent itemset mining methods. However, the inefficiency for calculating the intersection of itemsets makes it a time-consuming method, especially when the dataset has a large number of transactions. In this work, for the purpose of efficiency improvement, we proposed an approximate Eclat algorithm named HashEclat based on MinHash, which could quickly estimate the size of the intersection set, and adjust the parameters k, E and minSup to consider the tradeoff between accuracy of the mining results and execution time. The parameter k is the top-k parameter of one-permutation MinHash algorithm; the parameter E is the estimate error of one intersection size; the parameter minSup is the minimum support threshold. In many real situations, an approximate result with faster speed maybe more useful than ‘exact’ result. The theoretical analysis and experiment results that we present demonstrate that the proposed algorithm can output almost all of the frequent itemset with faster speed and less memory space.
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Metadata
Title
HashEclat: an efficient frequent itemset algorithm
Authors
Chunkai Zhang
Panbo Tian
Xudong Zhang
Qing Liao
Zoe L. Jiang
Xuan Wang
Publication date
04-01-2019
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 11/2019
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-018-00918-x

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