Fuzzy Association Rule Mining for Microarray Time Series Analysis
نویسندگان
چکیده
This paper describes how to discover dynamic relationships among genes from time series microarray data with association rule mining approach. To hold dynamic information in the rules, the association rules were extracted using the constraints that expression level of genes appear in the antecedent and change direction of expression level of genes in the consequent. Besides, we have applied fuzzy association rule mining techniques to deal with continuous numerical values. The fuzzy association rule mining alleviates hard boundary problem which usually occurs in the process of discretization of continuous numerical values. Yeast cell cycle data were analyzed with the proposed method, and we discovered several dynamic relationships among genes which can be supported by prior knowledge.
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