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Many data mining techniques have been proposed for parallel applications performance analysis, the most interesting being clustering analysis. Most cases have been used to detect processors with similar behavior. In previous work, we presented a different approach: clustering was used to detect the computation structure of the applications and how these different computation phases behave. In this...
Cluster analysis is an important tool for discovering the structures and patterns hidden in gene expression data. In this paper, a new algorithm for clustering gene expression profiles is proposed. In this method, we find natural clusters in the data based on a competitive learning strategy. Using partially known modes as constraints in our method, we reduce the sensitivity of the clustering procedure...
Recent advances of the DNA microarray technology allow monitoring gene expression profiles of thousands of genes simultaneously. However, the analysis and handling of such fast growing data is becoming the major bottleneck in the utilization of the technology. Clustering analysis is one of the most effective methods for analyzing such gene expression data. In this paper we first experimentally study...
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