A Robust k-Means Type Algorithm for Soft Subspace Clustering and Its Application to Text Clustering

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Provided by: Academy Publisher
Topic: Data Management
Format: PDF
Soft subspace clustering are effective clustering techniques for high dimensional datasets. Although several soft subspace clustering algorithms have been developed in recently years, its robustness should be further improved. In this paper, a novel soft subspace clustering algorithm RSSKM are proposed. It is based on the incorporation of the alternative distance metric into the framework of k-means type algorithm for soft subspace clustering and can automatically calculates the feature weights of each cluster in the clustering process.
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