Date of Award
2005
Publication Type
Master Thesis
Degree Name
M.Sc.
Department
Computer Science
Keywords
Computer Science.
Rights
info:eu-repo/semantics/openAccess
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Abstract
Rapid advances of microarray technologies are making it possible to analyze and manipulate large amounts of gene expression data. Clustering algorithms, such as hierarchical clustering, self-organizing maps, k-means clustering and fuzzy k-means clustering, have become important tools for expression analysis of microarray data. However, the need of prior knowledge of the number of clusters, k, and the fuzziness parameter, b, limits the usage of fuzzy clustering. Few approaches have been proposed for assigning best possible values for such parameters. In this thesis, we use simulated annealing and fuzzy k-means clustering to determine the optimal parameters, namely the number of clusters, k, and the fuzziness parameter, b. To assess the performance of our method, we have used synthetic and real gene experiment data sets. To improve our approach, two methods, searching with Tabu List and Shrinking the scope of randomization, are applied. Our results show that a nearly-optimal pair of k and b can be obtained without exploring the entire search space.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .Y37. Source: Masters Abstracts International, Volume: 44-03, page: 1419. Thesis (M.Sc.)--University of Windsor (Canada), 2005.
Recommended Citation
Yang, Wei, "Optimizing parameters in fuzzy k-means for clustering microarray data." (2005). Electronic Theses and Dissertations. 2953.
https://scholar.uwindsor.ca/etd/2953