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Proceedings Paper

Visualizing membership in multiple clusters after fuzzy c-means clustering
Author(s): Zach Cox; Julie A. Dickerson; Dianne H. Cook
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Paper Abstract

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.

Paper Details

Date Published: 3 May 2001
PDF: 9 pages
Proc. SPIE 4302, Visual Data Exploration and Analysis VIII, (3 May 2001); doi: 10.1117/12.424916
Show Author Affiliations
Zach Cox, Iowa State Univ. (United States)
Julie A. Dickerson, Iowa State Univ. (United States)
Dianne H. Cook, Iowa State Univ. (United States)


Published in SPIE Proceedings Vol. 4302:
Visual Data Exploration and Analysis VIII
Robert F. Erbacher; Philip C. Chen; Jonathan C. Roberts; Craig M. Wittenbrink; Matti Grohn, Editor(s)

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