A geometric visualization scheme for fuzzy-clustered DNA microarray data.

Date of Award


Publication Type

Master Thesis

Degree Name



Computer Science


Computer Science.



Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.


With the advent of a new and emerging technology, microarrays, clustering algorithms have become important in the analysis of gene expression. Fuzzy clustering, and in particular fuzzy k-means and Expectation Maximization, allow a gene to be assigned to multi-clusters with different degrees of membership. However, the memberships that result from fuzzy k-means, are rarely analyzed and visualized properly, but converted to 0-1 memberships. In this thesis, a model which allows to geometrically visualizing fuzzy-clustered DNA microarray data is presented. The model provides a geometric view by grouping the genes with similar cluster membership, and shows clear advantages over existing methods. The capabilities of the model for viewing and navigating inter-cluster relationships in a spatial manner are clearly demonstrated in general for well known datasets and public microarray datasets which are measured in experiments along time series.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .Z44. Source: Masters Abstracts International, Volume: 44-03, page: 1421. Thesis (M.Sc.)--University of Windsor (Canada), 2005.

This document is currently not available here.