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Identification of those genes that might anticipate the clinical behavior of different types of cancers is challenging due to availability of a smaller number of patient samples compared to huge number of genes, and the noisy nature of microarray data. After selection of some good genes based on signal-to-noise ratio, unsupervised learning like clustering and supervised learning like k-nearest neighbor (kNN) classifier are widely used in cancer researches to correlate the pathological behaviordoi:10.1145/1068009.1068081 dblp:conf/gecco/PaulI05 fatcat:tcu6v6oacjajne6bjb5yr5rdom