| 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. |
