Visualizing membership in multiple clusters after fuzzy c-means clustering

Zach Cox; Julie A. Dickerson; Dianne H. Cook

doi:10.1117/12.424916

3 May 2001 Visualizing membership in multiple clusters after fuzzy c-means clustering

Zach Cox, Julie A. Dickerson, Dianne H. Cook

Proceedings Volume 4302, Visual Data Exploration and Analysis VIII; (2001) https://doi.org/10.1117/12.424916
Event: Photonics West 2001 - Electronic Imaging, 2001, San Jose, CA, United States

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.

Citation Download Citation

Zach Cox, Julie A. Dickerson, and Dianne H. Cook "Visualizing membership in multiple clusters after fuzzy c-means clustering", Proc. SPIE 4302, Visual Data Exploration and Analysis VIII, (3 May 2001); https://doi.org/10.1117/12.424916

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