A survey of erasable itemset mining algorithms. (September 2014)

• Record Type: Journal Article
• Authors: Le, Tuong; Vo, Bay; Nguyen, Giang
• Is Part Of: Wiley interdisciplinary reviews. Volume 4:Number 5(2014)
• Page Start: 356
• Page End: 379
• Publication Date: 2014-09
• Subjects: Data mining -- Periodicals; 006.31205
• DOI: 10.1002/widm.1137
• Languages: English
• ISSNs: 1942-4787

Abstract

<abstract abstract-type="main" id="widm1137-abs-0001"> <title> <x xml:space="preserve">Abstract</x> </title> <p id="widm1137-para-0001">Pattern mining, one of the most important problems in data mining, involves finding existing patterns in data. This article provides a survey of the available literature on a variant of pattern mining, namely erasable itemset (EI) mining. EI mining was first presented in 2009 and META is the first algorithm to solve this problem. Since then, a number of algorithms, such as VME, MERIT, and dMERIT+, have been proposed for mining EI. MEI, proposed in 2014, is currently the best algorithm for mining EIs. In this study, the META, VME, MERIT, dMERIT+, and MEI algorithms are described and compared in terms of mining time and memory usage. <italic>WIREs Data Mining Knowl Discov</italic> 2014, 4:356–379. doi: 10.1002/widm.1137</p> <p>For further resources related to this article, please visit the <ext-link ext-link-type="uri" xlink:href="http://wires.wiley.com/remdoi.cgi?doi=10.1002/widm.1137" xlink:type="simple" xmlns:xlink="http://www.w3.org/1999/xlink">WIREs website</ext-link>.</p> <p id="widm1137-para-0003">Conflict of interest: The authors have declared no conflicts of interest for this article.</p> </abstract>