article

Frequent item set mining

Author:

Christian BorgeltAuthors Info & Claims

Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Volume 2, Issue 6

Pages 437 - 456

https://doi.org/10.1002/widm.1074

Published: 01 November 2012 Publication History

Abstract

Frequent item set mining is one of the best known and most popular data mining methods. Originally developed for market basket analysis, it is used nowadays for almost any task that requires discovering regularities between (nominal) variables. This paper provides an overview of the foundations of frequent item set mining, starting from a definition of the basic notions and the core task. It continues by discussing how the search space is structured to avoid redundant search, how it is pruned with the a priori property, and how the output is reduced by confining it to closed or maximal item sets or generators. In addition, it reviews some of the most important algorithmic techniques and data structures that were developed to make the search for frequent item sets as efficient as possible. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.

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cover image Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery

Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery Volume 2, Issue 6

November 2012

71 pages

ISSN:1942-4787

EISSN:1942-4795

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John Wiley & Sons, Inc.

United States

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Published: 01 November 2012

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Trasierras ALuna JFournier-Viger PVentura S(2024)Data heterogeneity's impact on the performance of frequent itemset mining algorithmsInformation Sciences: an International Journal10.1016/j.ins.2024.120981678:COnline publication date: 1-Sep-2024
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Li HChen LDemartini GZuccon GCulpepper JHuang ZTong H(2021)Cache-based GNN System for Dynamic GraphsProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482237(937-946)Online publication date: 26-Oct-2021
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