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While the vast majority of clustering algorithms are partitional, many real world datasets have inherently overlapping clusters. Several approaches to finding overlapping clusters have come from work on analysis of biological datasets. In this paper, we interpret an overlapping clustering model proposed by Segal et al. [23] as a generalization of Gaussian(More)
BACKGROUND Several years after sequencing the human genome and the mouse genome, much remains to be discovered about the functions of most human and mouse genes. Computational prediction of gene function promises to help focus limited experimental resources on the most likely hypotheses. Several algorithms using diverse genomic data have been applied to(More)
The complete set of mouse genes, as with the set of human genes, is still largely uncharacterized, with many pieces of experimental evidence accumulating regarding the activities and expression of the genes, but the majority of genes as yet still of unknown function. Within the context of the MouseFunc competition, we developed and applied two distinct(More)
— Algorithms have been recently developed for clustering microarray data that allow elements-usually genes-to belong to more than one cluster. The labellings that these algorithms produce are intuitively closer to the reality of biological processes, but are more difficult to analyze by traditional means. In this paper, we introduce an algorithm for(More)
The detection of overlapping patterns in unlabeled data sets referred as overlapping clustering is an important issue in data mining. In real life applications, overlapping clustering algorithm should be able to detect clusters with linear and non-linear separations between clusters. We propose in this paper an overlapping clustering method based k-means(More)
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