Fuzzy pattern tree approach for mining frequent patterns from gene expression data

Frequent pattern mining has been a focused theme in data mining research for over a decade. A lot of literature has been dedicated to this research and huge amount of work has been made, ranging from efficient and scalable algorithms for frequent item set mining in transaction databases to numerous research frontiers. Frequent pattern mining (FPM) has been applied successfully in business and scientific data for discovering interesting association patterns, and is becoming a promising strategy in microarray gene expression analysis. As we know, Fuzzy logic provides a mathematical framework that is compatible with poorly quantitative yet qualitatively significant data. In this paper, we have fuzzified our original dataset and have applied various frequent pattern mining techniques to discover meaningful frequent patterns. Also, we have drawn a clear comparison of the frequent pattern mining techniques in the original and the fuzzified data in terms of parameters like runtime of the algorithm and the number of frequent patterns generated. As a result, it was found that the fuzzified set is capable of discovering a large number of frequent patterns and have a better running time capability.