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SIGMOD
2005
ACM

Mining Top-k Covering Rule Groups for Gene Expression Data

12 years 5 months ago
Mining Top-k Covering Rule Groups for Gene Expression Data
In this paper, we propose a novel algorithm to discover the topk covering rule groups for each row of gene expression profiles. Several experiments on real bioinformatics datasets show that the new top-k covering rule mining algorithm is orders of magnitude faster than previous association rule mining algorithms. Furthermore, we propose a new classification method RCBT. RCBT classifier is constructed from the top-k covering rule groups. The rule groups generated for building RCBT are bounded in number. This is in contrast to existing rule-based classification methods like CBA [19] which despite generating excessive number of redundant rules, is still unable to cover some training data with the discovered rules. Experiments show that the RCBT classifier can match or outperform other state-of-the-art classifiers on several benchmark gene expression datasets. In addition, the top-k covering rule groups themselves provide insights into the mechanisms responsible for diseases directly.
Gao Cong, Kian-Lee Tan, Anthony K. H. Tung, Xin Xu
Added 08 Dec 2009
Updated 08 Dec 2009
Type Conference
Year 2005
Where SIGMOD
Authors Gao Cong, Kian-Lee Tan, Anthony K. H. Tung, Xin Xu
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