Mining Top-K Frequent Closed Patterns from Gene Expression Data

Analyzing microarray gene expression data provides biologists deep insights into gene functions and gene regulatory network. In this paper, we propose a novel efficient algorithm FCPminer to mine top-k frequent closed patterns (FCPs) of higher support with length no less than minL from gene expression data. FCPminer employs a prefix fp-tree data structure, with top-down best first search strategy, such that FCPs of adequate length with highest supports are firstly mined. Compared with existing top-k FCP mining algorithms, FCPminer is much more efficient as it avoids expanding nodes with inadequate length (less than minL) or low support (ranked below top-k) during mining process. In addition, FCPminer further improves mining efficiency by employing a hash-based closedness checking method. Experimental results on real biological and synthetic data show that our proposed FCPminer outperforms existing state-of the art algorithms with high efficiency, especially for large and dense datasets.