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An evolutionary computational model applied to cluster analysis of DNA microarray data
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DOI：10.1016/j.eswa.2012.10.061.png)
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This paper proposes a new hierarchical clustering method using genetic algorithms for the analysis of gene expression data. This method is based on the mathematical proof of several results, showing its effectiveness with regard to other clustering methods. Genetic algorithms applied to cluster analysis have disclosed good results on biological data and many studies have been carried out in this sense, although most of them are focused on partitional clustering methods. Even though there are few studies that attempt to use genetic algorithms for building hierarchical clustering, they do not include constraints that allow us to reduce the complexity of the problem. Therefore, these studies become intractable problems for large data sets. On the other hand, the deterministic hierarchical clustering methods generally face the problem of convergence towards local optimums due to their greedy strategy. The method introduced here is an alternative to solve some of the problems existing methods face. The results of the experiments have shown that our approach can be very effective in cluster analysis of DNA microarray data. (c) 2012 Elsevier Ltd. All rights reserved.
Keyword:
DNA microarray data
Genetic algorithm
Data mining
Hierarchical clustering
Cluster validity
Combinatorial optimization
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期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
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引用论文
An evolutionary clustering algorithm for gene expression microarray data analysis一种用于基因表达微阵列数据分析的进化聚类算法
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