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WIREs Data Mining Knowl Discov

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A survey of erasable itemset mining algorithms

Overview

Tuong Le, Bay Vo, Giang Nguyen

Published Online: Oct 29 2014

DOI: 10.1002/widm.1137

Full article on Wiley Online Library: HTML PDF

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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. WIREs Data Mining Knowl Discov 2014, 4:356–379. doi: 10.1002/widm.1137 This article is categorized under: Algorithmic Development > Association Rules

Efficient algorithm for subtracting two dPidsets.

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Complete set of erasable itemsets identified by dMERIT+ for DBe with ξ = 16%.

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EIs of node {e} for DBe with ξ = 16%.

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Erasable 2‐itemsets of node {e} for DBe with ξ = 16%.

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Erasable 1‐itemsets and their NC′_Set for DBe with ξ = 16%.

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dMERIT+ algorithm.

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Efficient method for subtracting two dNC'_Sets.

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Result of MERIT+ for DBe with ξ = 16%.

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Erasable 1‐itemsets, E1, and its NC_Sets for DBe with ξ = 16%.

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MERIT algorithm.

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Efficient method for combining two NC_Sets.

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WPPC‐tree for DBe with ξ = 16%.

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Illustration of WPPC‐tree construction process for DBe.

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WPPC‐tree construction algorithm.

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VME algorithm.

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META algorithm.

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Memory usage for T10I4D100K dataset.

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Memory usage for Pumsb dataset.

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Memory usage for Mushroom dataset.

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Memory usage for Connect dataset.

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Memory usage for Chess dataset.

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Memory usage for Accidents dataset.

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Mining time for T10I4D100K dataset.

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Mining time for Pumsb dataset.

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Mining time for Mushroom dataset.

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Mining time for Connect dataset.

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Mining time for Chess dataset.

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Mining time for Accidents dataset.

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Tree of EIs obtained using dPidset without sorting erasable 1‐itemsets for DBe with ξ = 16%.

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Tree of EIs obtained using pidset for DBe with ξ = 16%.

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Tree of all EIs obtained by MEI for DBe with ξ = 16%.

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All EIs of node {e} for DBe with ξ = 16%.

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Erasable 3‐itemsets of node {ed} for DBe with ξ = 16%.

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Erasable 2‐itemsets of node {e} for DBe with ξ = 16%.

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MEI algorithm.

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