DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2013.59
Zhiwen Yu , South China University of Technology, Guangzhou and Hong Kong Polytechnic University, Hong Kong
Hantao Chen , South China University of Technology, Guangzhou
Jane You , Hong Kong Polytechnic University, Hong Kong
Guoqiang Han , South China University of Technology, Guangzhou
Le Li , South China University of Technology, Guangzhou
In order to further improve the performance of tumor clustering from bio-molecular data, we introduce the fuzzy theory into the cluster ensemble framework for tumor clustering from bio-molecular data, and propose four kinds of hybrid fuzzy cluster ensemble frameworks, named as HFCEF-I, HFCEF-II, HFCEF-III and HFCEF-IV respectively, to identify samples which belong to different types of cancers. The difference between HFCEF-I and HFCEF-II is that they adopt different ensemble generator approaches to generate a set of fuzzy matrices in the ensemble. Specifically, HFCEF-I applies the affinity propagation algorithm (AP) to perform clustering on the sample dimension, and generates a set of fuzzy matrices in the ensemble based on the fuzzy membership function and base samples selected by AP. HFCEF-II adopts AP to perform clustering on the attribute dimension, generates a set of subspaces, and obtains a set of fuzzy matrices in the ensemble by performing fuzzy c-means on subspaces. Compared with HFCEF-I and HFCEF-II, HFCEF-III and HFCEF-IV consider the characteristics of HFCEF-I and HFCEF-II. HFCEF-III combines HFCEF-I and HFCEF-II in a serial way, while HFCEF-IV integrates HFCEF-I and HFCEF-II in a concurrent way.
Health, Computer Applications, Life and Medical Sciences, Biology and genetics
G. Han, H. Chen, L. Li, Z. Yu and J. You, "Hybrid Fuzzy Cluster Ensemble Framework for Tumor Clustering from Bio-molecular Data," in IEEE/ACM Transactions on Computational Biology and Bioinformatics.