Visualizing membership in multiple clusters after fuzzy c-means clustering
Zach Cox, Julie A. Dickerson, Dianne Cook · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2001
Cluster analysis is an exploratory data mining technique that involves grouping data points together based on their similarity. Objects or data points are often similar to points in more than one cluster; this is typically quantified by a measure of membership in a cluster, called fuzziness. Visualizing membership degrees in multiple clusters is the main topic of this paper. We use Orca, a java-based high-dimensional visualization environment, as the implementation platform to test several approaches, including convex hulls, glyphs, coloring schemes, and 3D plots.