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Cancer is a heterogeneous disease caused by genomic aberrations and characterized by significant variability in clinical outcomes and response to therapies. In the meantime, cancer is a complex disease driven by the interaction of multiple genes. It has been reported that the copy number of individual gene is not sufficient to define cancer subtypes and predict responses to treatments. The recent advancement in array comparative genomic hybridization (aCGH) research has significantly improved tumor identification using DNA copy number data. We have developed a Markov model-based algorithm for identifying tumor subtype using array CGH data. It showed this HMMC method has a 50% lower error rate than that of the nonnegative matrix factorization (NMF) method. The HMMC method was able to locate the optimal number of groups automatically when applied to the glioma aCGH data. The resulting clustering of glioma samples has shown strong correlation to clinical data on survival rate. The newly developed algorithm would potentially have wide applications in tumor subtype identification, genomic signature discovery, and diagnostic and prognostic biomarker search.