An entropy weighting mixture model for subspace clustering of high-dimensional data
Pattern Recognition Letters
Class-dependent projection based method for text categorization
Pattern Recognition Letters
Advancing data clustering via projective clustering ensembles
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
International Journal of Metadata, Semantics and Ontologies
Projective clustering ensembles
Data Mining and Knowledge Discovery
Projected-prototype based classifier for text categorization
Knowledge-Based Systems
International Journal of Metadata, Semantics and Ontologies
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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. Second, a general objective criterion used with $k$-means type projective clustering is presented based on the model. Finally, the expressions to learn model parameters are derived and then used in a new algorithm named FPC to perform fuzzy clustering on high dimensional data. The experimental results on document clustering show the effectiveness of the proposed clustering model.