Frequent closed informative itemset mining

Huaiguo Fu, Mícheál Ó Foghlú, Willie Donnelly

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

Abstract

In recent years, cluster analysis and association analysis have attracted a lot of attention for large data analysis such as biomedical data analysis. This paper proposes a novel algorithm of frequent closed itemset mining. The algorithm addresses two challenges of data mining: mining large and high dimensional data and interpreting the results of data mining. Frequent itemset mining is the key task of association analysis. The algorithm is based on concept lattice structure so that frequent closed itemsets can be generated to reduce the complicity of mining all frequent itemsets and each frequent closed itemset has more information to facilitate interpretation of mining results. From this feature, the paper also discusses the extension of the algorithm for cluster analysis. The experimental results show the efficiency of this algorithm.

Original languageEnglish
Title of host publicationProceedings - 2007 International Conference on Computational Intelligence and Security, CIS 2007
Pages232-236
Number of pages5
DOIs
Publication statusPublished - 2007
Event2007 International Conference on Computational Intelligence and Security, CIS'07 - Harbin, Heilongjiang, China
Duration: 15 Dec 200719 Dec 2007

Publication series

NameProceedings - 2007 International Conference on Computational Intelligence and Security, CIS 2007

Conference

Conference2007 International Conference on Computational Intelligence and Security, CIS'07
Country/TerritoryChina
CityHarbin, Heilongjiang
Period15/12/200719/12/2007

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