HashMax: A new method for mining maximal frequent itemsets

Natalia Vanetik, Ehud Gudes

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Mining maximal frequent itemsets is a fundamental problem in many data mining applications, especially in the case of dense data when the search space is exponential. We propose a top-down algorithm that employs hashing techniques, named HashMax, in order to generate maximal frequent itemsets efficiently. An empirical evaluation of our algorithm in comparison with the state-of-the-art maximal frequent itemset generation algorithm Genmax shows the advantage of HashMax in the case of dense datasets with a large amount of maximal frequent itemsets.

Original languageEnglish
Title of host publicationKDIR 2011 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval
Pages140-145
Number of pages6
StatePublished - 1 Dec 2011
EventInternational Conference on Knowledge Discovery and Information Retrieval, KDIR 2011 - Paris, France
Duration: 26 Oct 201129 Oct 2011

Publication series

NameKDIR 2011 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval

Conference

ConferenceInternational Conference on Knowledge Discovery and Information Retrieval, KDIR 2011
Country/TerritoryFrance
CityParis
Period26/10/1129/10/11

Keywords

  • Maximal frequent itemset mining

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