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In order to perform successful diagnosis and treatment of cancer, discovering and classifying cancer types correctly is essential. Most of the existing works adopt single clustering algorithms to perform class discovery from bio-molecular data. Unfortunately, single clustering algorithms have limitations, which are lack of the robustness, stableness and accuracy. In this paper, we develop a new probabilistic subspace ensemble framework known as PSEFminer for cancer microarray data analysis. PSEFminer integrates the probabilistic subspace generator, the self-organizing map(SOM) and the normalized cut algorithm into the ensemble framework to discover the underlying structure from cancer microarray data. The experiments in cancer datasets show that (i) the probabilistic subspace generator plays an important role to improve the performance of PSEFminer; (ii) PSEFminer outperforms most of the state-of-the-art cluster ensemble algorithms when applied to cancer gene expression data.