Model-Based Clustering and Data Transformations for Gene Expression Data (2001) [2 citations — 0 self]
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author = {Adrian E. Raftery and Ka Yee Yeung and Ka Yee Yeung and Chris Fraley and Chris Fraley and Alejandro Murua and Alejandro Murua and Walter L. Ruzzo and Walter L. Ruzzo},
title = {Model-Based Clustering and Data Transformations for Gene Expression Data},
journal = {Bioinformatics},
year = {2001},
volume = {17},
pages = {2001}
}
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Abstract:
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. This Gaussian mixture model has been shown to be a powerful tool for many applications. In addition, 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.
