A new feature weighted self-adaptive FCM clustering algorithm for gene expression data

摘要

When dealing with gene expression data, FCM algorithm has the following defects: sensitiveness to the initial cluster centers, need to input the cluster number preliminary and doesn't consider the different contributions of attributes of gene expression data and so on. Accordingly, this paper presents a new feature weighted self-adaptive FCM algorithm handling gene expression data. First introduce a dataset pre-processing algorithms, then determine weights of all features of gene expression data set based on message-entropy, finally introduce the features' weights into the objective function of FCM algorithm. Experimental results show that the new algorithm significantly improves the effectiveness of taxonomic notes for gene expression data.