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Erschienen in:
2006 | OriginalPaper | Buchkapitel
A Fuzzy Subspace Algorithm for Clustering High Dimensional Data
verfasst von : Guojun Gan, Jianhong Wu, Zijiang Yang
Erschienen in: Advanced Data Mining and Applications
Verlag: Springer Berlin Heidelberg
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In fuzzy clustering algorithms each object has a fuzzy membership associated with each cluster indicating the degree of association of the object to the cluster. Here we present a fuzzy subspace clustering algorithm, FSC, in which each dimension has a weight associated with each cluster indicating the degree of importance of the dimension to the cluster. Using fuzzy techniques for subspace clustering, our algorithm avoids the difficulty of choosing appropriate cluster dimensions for each cluster during the iterations. Our analysis and simulations strongly show that FSC is very efficient and the clustering results produced by FSC are very high in accuracy.