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Biclustering has become an important data mining technique for microarray gene expression analysis and profiling, as it provides a local view of the hidden relationships in data, unlike a global view provided by conventional clustering techniques. This technique, in contrast to the conventional clustering techniques, helps in identifying a subset of the genes and a subset of the experimental conditions that together exhibit co-related pattern. In this paper, a biclustering technique using weighted crossing minimization paradigm is proposed, which can mine significant patterns by employing a local search instead of a global search of the input data matrix.
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