Visualizing membership in multiple clusters after fuzzy c-means clustering

Z. Cox, J. A. Dickerson, D. Cook

Research output: Contribution to journalConference articleOtherpeer-review

5 Citations (Scopus)

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 3-dimensional plots.

Original languageEnglish
Pages (from-to)60-68
Number of pages9
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume4302
DOIs
Publication statusPublished - 1 Jan 2001
EventVisual Data Exploration and Analysis VIII - San Jose, CA, United States of America
Duration: 22 Jan 200123 Jan 2001

Keywords

  • Clustering
  • Fuzzy c-means
  • Java
  • Orca
  • Visualizing uncertainty

Cite this

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Visualizing membership in multiple clusters after fuzzy c-means clustering. / Cox, Z.; Dickerson, J. A.; Cook, D.

In: Proceedings of SPIE - The International Society for Optical Engineering, Vol. 4302, 01.01.2001, p. 60-68.

Research output: Contribution to journalConference articleOtherpeer-review

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