Missing Clusters Indicate Poor Estimates or Guesses of a Proper Fuzzy Exponent
WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory
Fuzzy-Adaptive-Subspace-Iteration-Based Two-Way Clustering of Microarray Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A stochastic learning-to-rank algorithm and its application to contextual advertising
Proceedings of the 20th international conference on World wide web
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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 and fuzzy kmeans, 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 the best possible values for such parameters. In this paper, 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. Our results show that a nearly-optimal pair of k and b can be obtained without exploring the entire search space.