A Novel Scheme for Visualization of Fuzzy-clustered Data
نویسندگان
چکیده
Many clustering methods have been proposed, including fuzzy k-means, which allows an object to be assigned to multi-clusters with different degree of membership. However, the memberships that result from fuzzy k-means, are rarely analyzed and visualized properly, but converted to 0-1 memberships. In this paper, we propose a new approach to visualize fuzzy-clustered data. The scheme provides a geometric view by grouping the objects with similar cluster membership, and shows clear advantages over existing methods, demonstrating its capabilities for viewing and navigating inter-cluster relationships in a spatial manner.
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تاریخ انتشار 2004