A Soft Subspace Clustering Method for Text Data Using a Probability Based Feature Weighting Scheme

Clustering methods aim to find clusters or groups of similar objects in a given set of data. Common soft subspace clustering methods for text data find different clusters in subspaces using a weighted distance measure. The weighting scheme heavily affects the clustering performance and requires special consideration. Since text data has semantic information along with syntactic information, a weighting scheme, which uses semantic information, is more likely to generate a better clustering solution.

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