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Clustering high dimensional data is a big challenge in data mining due to the curse of dimensionality. To solve this problem, projective clustering has been defined as an extension of traditional clustering that seeks to find projected clusters in subsets of dimensions of a data space. In this paper, the problem of modeling projected clusters is first discussed, and an extended Gaussian model is proposed...
Existing indexes are usually composed of two factors: compactness and separation, although some authors use overlap in place of compactness. In this paper, we propose a new index based on a combination of the three factors. This index is potentially more efficient in dealing with simultaneous presence of overlapped clusters, point clusters and low-density clusters. Experimental results support out...
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