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.
|Number of pages||9|
|Journal||Proceedings of SPIE - The International Society for Optical Engineering|
|Publication status||Published - 1 Jan 2001|
|Event||Visual Data Exploration and Analysis VIII - San Jose, CA, United States of America|
Duration: 22 Jan 2001 → 23 Jan 2001
- Fuzzy c-means
- Visualizing uncertainty