Time series inference from clustering

This paper presents a toolbox for analyzing inferences drawn from clustering. 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. These classes represent different random vectors. Each random vector 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 vectors. Clustering algorithms are evaluated based on class variance and performance improvement with respect to increasing numbers of experimental replications. The study is presented on a website, which includes error tables and graphs, confusion matrices, principle-component plots, and validation measures. There, the toolbox is applied to gene-expression clustering based on cDNA microarrays using real data.