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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. We present the novel idea of modelling the gene expression data as a weighted bipartite graph between genes and experimental conditions in order to rearrange the vertices in one layer of this graph. Using this model, an efficient biclustering technique is developed that can mine different types of biclusters and works well in practice for simulated and real world data. The experimental results demonstrate that, our method is scalable to practical gene expression data and has superiority over other similar algorithms in terms of accuracy and computational efficiency.

BiCross : A Biclustering Technique for Gene Expression Data using One Layer Fixed Weighted Bipartite Graph Crossing Minimization