On discovery of maximal con-dent rules without support pruning in microarray data.

Tara Mcintosh, Sanjay Chawla

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Microarray data provides a pcrfcct riposte to the original assumption underlying association rule mining - large but sparse transaction sets. Tn a typical microarray the number of columns (genes) is an order of magnitude larger than the number of rows (experiments). A new family of row enumerated rule mining algorithms have emerged to facilitate mining in dense sets. However, to date, all the algorithms proposed to mine expression relationships alone rely on the support measure to prune the search space. This is a major shortcoming as it results in the pruning of many potentially interesting rules which have low support but high confidence. In this paper we propose the MaxConf algorithm which exploits the weak downward closure of confidence to directly mine for high confidence rules. We also provide a means to evaluate the biological significance of the gene relationships identified. An evaluation of MaxConf with RERTT on the database BIND shows that their recall is 94% and .15% respectively.

Original languageEnglish
Title of host publicationProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Pages37-45
Number of pages9
DOIs
Publication statusPublished - 2005
Externally publishedYes
Event5th International Workshop on Bioinformatics, BIOKDD 2005 - In Conjunction with 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2005 - Chicago, IL, United States
Duration: 21 Aug 200521 Aug 2005

Other

Other5th International Workshop on Bioinformatics, BIOKDD 2005 - In Conjunction with 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2005
CountryUnited States
CityChicago, IL
Period21/8/0521/8/05

Fingerprint

Microarrays
Genes
Association rules
Experiments

Keywords

  • Association rules
  • Maximum confidence
  • Microarray
  • Row-enumeration

ASJC Scopus subject areas

  • Software
  • Information Systems

Cite this

Mcintosh, T., & Chawla, S. (2005). On discovery of maximal con-dent rules without support pruning in microarray data. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 37-45) https://doi.org/10.1145/1134030.1134038

On discovery of maximal con-dent rules without support pruning in microarray data. / Mcintosh, Tara; Chawla, Sanjay.

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2005. p. 37-45.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Mcintosh, T & Chawla, S 2005, On discovery of maximal con-dent rules without support pruning in microarray data. in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp. 37-45, 5th International Workshop on Bioinformatics, BIOKDD 2005 - In Conjunction with 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2005, Chicago, IL, United States, 21/8/05. https://doi.org/10.1145/1134030.1134038
Mcintosh T, Chawla S. On discovery of maximal con-dent rules without support pruning in microarray data. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2005. p. 37-45 https://doi.org/10.1145/1134030.1134038
Mcintosh, Tara ; Chawla, Sanjay. / On discovery of maximal con-dent rules without support pruning in microarray data. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2005. pp. 37-45
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