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3 May 2001 Visualizing membership in multiple clusters after fuzzy c-means clustering
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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.
© (2001) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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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