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In many applications of document clustering, a document may include multiple topics and thus may relate to multiple categories at the same time. Most of the existing subspace clustering algorithms can only perform hard clustering on document collections. In this paper, a fuzzy algorithm named R-FPC is introduced for document clustering. The algorithm discovers soft partitions of a data set in the...
This paper presents an approach which extends a particle swarm optimizer for variable weighting (PSOVW) to handle the problem of text clustering, called text clustering via particle swarm optimization (TCPSO). PSOVW has been exploited for evolving optimal feature weights for clusters and has demonstrated to improve the clustering quality of high-dimensional data. However, when applying it for text...
Clustering high-dimensional data has been a major challenge due to the inherent sparsity of the points. Most existing clustering algorithms become substantially inefficient if the required similarity measure is computed between data points in the full-dimensional space. To address this problem, a number of projected clustering algorithms have been proposed. However, most of them encounter difficulties...
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...
As an important issue in cluster analysis, cluster validation is the process of evaluating performance of clustering algorithms under varying input conditions. Many existing methods address clustering results of low-dimensional data. This paper presents new solution to the problem of cluster validation for subspace clustering on high dimensional data. We first propose two new measurements for the...
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