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Abstract: Biclustering is an important method in DNA microarray analysis which can be applied when only a subset of genes is co-expressed in a subset of conditions. Unlike standard clustering analyses, biclustering methodology can perform simultaneous classification on two dimensions of genes and conditions in a microarray data matrix. However, the performance of biclustering algorithms is affected by the inherent noise in data, types of biclusters and computational complexity. In this paper, we present a geometric biclustering method based on the Hough transform and the relaxation labeling technique. Unlike many existing biclustering algorithms, we first consider the biclustering patterns through geometric interpretation. Such a perspective makes it possible to unify the formulation of different types of biclusters as hyperplanes in spatial space and facilitates the use of a generic plane finding algorithm for bicluster detection. In our algorithm, the Hough transform is employed for hyperplane detection in sub-spaces to reduce the computational complexity. Then sub-biclusters are combined into larger ones under the probabilistic relaxation labeling framework. Our simulation studies demonstrate the robustness of the algorithm against noise and outliers. In addition, our method is able to extract biologically meaningful biclusters from real microarray gene expression data. [Copyright &y& Elsevier]

Abstract: Biclustering is an important tool in microarray analysis when only a subset of genes co-regulates in a subset of conditions. Different from standard clustering analyses, biclustering performs simultaneous classification in both gene and condition directions in a microarray data matrix. However, the biclustering problem is inherently intractable and computationally complex. In this paper, we present a new biclustering algorithm based on the geometrical viewpoint of coherent gene expression profiles. In this method, we perform pattern identification based on the Hough transform in a column-pair space. The algorithm is especially suitable for the biclustering analysis of large-scale microarray data. Our studies show that the approach can discover significant biclusters with respect to the increased noise level and regulatory complexity. Furthermore, we also test the ability of our method to locate biologically verifiable biclusters within an annotated set of genes. [Copyright &y& Elsevier]

Market segmentation plays a crucial role in product design and development. However, conventional segmentation approaches based on one-way cluster analysis techniques have met two special challenges in practice. First, conventional approaches that derive a global result rather than a local one fail to cluster customers into such groups who have similar characteristics on a fraction of variables. Second, since there is no formal mechanism to select appropriate segmentation variables, different combination of variables will obtain different segmentation results, which makes the approaches not quite convincing. To overcome the two limitations, a novel biclustering-based market segmentation method by using customer pain points is proposed in this paper. Different from one-way algorithms clustering only rows or only columns, biclustering algorithms cluster both rows associated with customers and columns associated with customer pain points simultaneously to identify homogenous subgroups of customers with common characteristics towards a subset of segmentation variables. In addition, customer pain points are used to replace traditional segmentation variables in the presented method, which makes the results more reasonable. Subsequently, an illustrated example is studied to demonstrate the effectiveness of the presented method. [ABSTRACT FROM AUTHOR]