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 inter-cluster relationships in a spatial manner.