A geometric framework to visualize fuzzy-clustered data

— Fuzzy clustering methods have been widely used in many applications. These methods, including fuzzy k-means and Expectation Maximization, allow an object to be assigned to multi-clusters with different degrees of membership. However, the memberships that result from fuzzy clustering algorithms are difficult to analyze and visualize, and usually are converted to 0-1 memberships. In this paper, we propose a geometric framework to visualize fuzzy-clustered data. The scheme provides a geometric visualization by grouping the objects with similar cluster membership, and shows clear advantages over existing methods, demonstrating its capabilities for viewing and navigating intercluster relationships in a spatial manner.

• Type: Conference
• Year: 2005
• Where: SCCC
• Authors: Yuanquan Zhang, Luis Rueda
• Keywords: Fuzzy Clustering, Fuzzy Clustering Algorithms, Fuzzy Clustering Methods, SCCC 2005