Inference from Clustering with Application to Gene-Expression Microarrays

There are many algorithms to cluster sample data points based on nearness or a similarity measure. Often the implication is that points in different clusters come from different underlying classes, whereas those in the same cluster come from the same class. Stochastically, the underlying classes represent different random processes. The inference is that clusters represent a partition of the sample points according to which process they belong. This paper discusses a model-based clustering toolbox that evaluates cluster accuracy. Each random process is modeled as its mean plus independent noise, sample points are generated, the points are clustered, and the clustering error is the number of points clustered incorrectly according to the generating random processes. Various clustering algorithms are evaluated based on process variance and the key issue of the rate at which algorithmic performance improves with increasing numbers of experimental replications. The model means can be selec...

• Type: Journal
• Year: 2002
• Where: JCB
• Authors: Edward R. Dougherty, Junior Barrera, Marcel Brun, Seungchan Kim, Roberto Marcondes Cesar Junior, Yidong Chen, Michael L. Bittner, Jeffrey M. Trent
• Keywords: Algorithms, Clustering Algorithm, JCB 2002, Random Processes