A distributed algorithm of density-based subspace frequent closed itemset mining

Fu Huaiguo, Ó Foghlú Mícheál

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

3 Citations (Scopus)

Abstract

Large, dense-packed and high-dimensional data mining is one challenge of frequent closed itemset mining for association analysis, although frequent closed itemset mining is an efficient approach to reduce the complexity of mining frequent itemsets. This paper proposes a distributed algorithm to address the challenge of discovering frequent closed itemsets in large, dense-packed and high-dimensional data. The algorithm partitions the search space of frequent closed itemsets into independent nonoverlapping subspaces that can be extracted independently to generate frequent closed itemsets. The algorithm can generate frequent closed item-sets according to dense priority: the closed itemset more dense or more frequent will be generated preferentially. The experimental results show the algorithm is efficient to extract frequent closed itemsets in large data.

Original languageEnglish
Title of host publicationProceedings - 10th IEEE International Conference on High Performance Computing and Communications, HPCC 2008
Pages750-755
Number of pages6
DOIs
Publication statusPublished - 2008
Event10th IEEE International Conference on High Performance Computing and Communications, HPCC 2008 - Dalian, China
Duration: 25 Sep 200827 Sep 2008

Publication series

NameProceedings - 10th IEEE International Conference on High Performance Computing and Communications, HPCC 2008

Conference

Conference10th IEEE International Conference on High Performance Computing and Communications, HPCC 2008
Country/TerritoryChina
CityDalian
Period25/09/200827/09/2008

Keywords

  • Association analysis
  • Concept lattice
  • Distributed algorithm
  • Frequent closed itemset mining
  • Partition

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