Robust Fuzzy Cluster Ensemble on Cancer Gene Expression Data

Abstract

Noise remains a particularly challenging and ubiquitous problem in cancer gene expression data clustering research, which may cause inaccurate results and mislead the underlying biological meanings. A clustering method that is robust to noise is highly desirable. No one clustering method performs best across all data sets despite a vast number of methods available. Cluster ensemble provides an approach to automatically combine results from multiple clustering methods for improving robustness and accuracy. We have proposed a novel noise robust fuzzy cluster ensemble algorithm. It employs an improved fuzzy clustering approach with different initializations as its base clusterings to avoid or alleviate the effects of noise in data sets. Its results show effective improvements over most examined noisy real cancer gene expression data sets when compared with most evaluated benchmark clustering methods, it is the top performer on three of the eight data sets, more than any other methods evaluated, and it performs well on most of the other data sets. Also, our fuzzy cluster ensemble is robust on highly noisy synthetic data sets. Moreover, it is computationally efficient.