Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) (2006)
Hong Kong, China
Dec. 18, 2006 to Dec. 22, 2006
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDMW.2006.50
Meghana Deodhar , University of Texas at Austin
Joydeep Ghosh , University of Texas at Austin
Most clustering algorithms are partitional in nature, assigning each data point to exactly one cluster. However, several real world datasets have inherently overlapping clusters in which a single data point can belong entirely to more than one cluster. This is often the case with gene microarray data since it is possible for a single gene to participate in more than one biological process. This paper deals with a novel application of consensus clustering for detecting overlapping clusters. Our approach takes advantage of the fact that results obtained by applying different clustering algorithms to the same dataset could be different and a consensus across these results could be used to detect overlapping clusters. Moreover we extend a popular model selection approach called X-means  to detect the inherent number of overlapping clusters in the data.
M. Deodhar and J. Ghosh, "Consensus Clustering for Detection of Overlapping Clusters in Microarray Data," Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)(ICDMW), Hong Kong, China, 2006, pp. 104-108.