Combining hierarchical clustering and self-organizing maps for exploratory analysis of gene expression patterns
Combining hierarchical clustering and self-organizing maps for exploratory analysis of gene expression patterns
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DOI：
10.1021/pr025521v
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发表时间：
2002-09-01
影响因子：
4.4
通讯作者：
Dopazo, J
中科院分区：
文献类型：
--
作者：
Herrero, J;Dopazo, J
Self-organizing maps (SOM) constitute an alternative to classical clustering methods because of its linear run times and superior performance to deal with noisy data. Nevertheless, the clustering obtained with SOM is dependent on the relative sizes of the clusters. Here, we show how the combination of SOM with hierarchical clustering methods constitutes an excellent tool for exploratory analysis of massive data like DNA microarray expression patterns.
