Item – Theses Canada

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OCLC number
190775238
Link(s) to full text
LAC copy
LAC copy
Author
Yang, Wei,1973-
Title
Optimizing parameters in fuzzy k-means for clustering microarray data.
Degree
M. Sc. -- University of Windsor, 2005
Publisher
Ottawa : Library and Archives Canada = Bibliothèque et Archives Canada, [2006]
Description
2 microfiches
Notes
Includes bibliographical references.
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.
ISBN
0494098406
9780494098400