We present a novel probabilistic latent variable model to perform linear dimensionality reduction on data sets which contain clusters. We prove that the maximum likelihood solution...
Background: The previous studies of genome-wide expression patterns show that a certain percentage of genes are cell cycle regulated. The expression data has been analyzed in a nu...
Scientific investigations have to deal with rapidly growing amounts of data from simulations and experiments. During data analysis, scientists typically want to extract subsets o...
Alexandru Romosan, Doron Rotem, Arie Shoshani, Der...
Despite its potential advantages for fMRI analysis, fuzzy C-means (FCM) clustering suffers from limitations such as the need for a priori knowledge of the number of clusters, and ...
This paper describes an evolutionary clustering algorithm, which can partition a given dataset automatically into the optimal number of groups through one shot of optimization. The...