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Rapid technological advancements in microarray analysis continue to generate enormous amounts of genomic data. However, neither hardware nor software computational capabilities have kept pace with this drastic increase. This paper presents a novel framework designed to achieve fast, robust, and accurate (biologically-significant) multi-class classification of gene expression data using distributed knowledge discovery and computational integration routines, specifically for cancer applications. The proposed paradigm consists of the following key computational steps: (a) preprocessing normalization and discretization of gene expression data, (b) partition data using two methods: overlapped windows and adaptive selection, (c) perform association rule discovery on partitioned data-spaces using FP-growth method, (d) integrate derived association rules on distributed processor nodes using a novel knowledge integration algorithm, (e) further prune rules to reduce dimensionality using parametric significance estimation, and (f) cluster remaining rules using a novel clustering algorithm for enhanced visualization and interpretation of discovered gene rule sets.