作者:
KY Yeung，C Fraley，A Murua，AE Raftery，WL Ruzzo
关键词:
Gene Expression Profiling ； Models； Statistical ； 模型； 统计学
摘要:
Clustering is a useful exploratory technique for the analysis of gene expression data. Many different heuristic clustering algorithms have been proposed in this context. Clustering algorithms based on probability models offer a principled alternative to heuristic algorithms. In particular, model-based clustering assumes that the data is generated by a finite mixture of underlying probability distributions such as multivariate normal distributions. The issues of selecting a ‘good’ clustering method and determining the ‘correct’ number of clusters are reduced to model selection problems in the probability framework. Gaussian mixture models have been shown to be a powerful tool for clustering in many applications.
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