Hierarchical clustering of text collections is a key problem in document management and retrieval. In partitional hierarchical clustering, which is more efficient than its agglomerative counterpart, the entire collection is split into clusters and the individual clusters are further split until a heuristically-motivated termination criterion is met. In this paper, we define the BIC-means algori...

We formulate weighted graph clustering as a prediction problem1: given a subset of edge weights we analyze the ability of graph clustering to predict the remaining edge weights. This formulation enables practical and theoretical comparison of different approaches to graph clustering as well as comparison of graph clustering with other possible ways to model the graph. We adapt the PAC-Bayesian ...

Clustering analysis is an important tool in studying gene expression data. The Bayesian hierarchical clustering (BHC) algorithm can automatically infer the number of clusters and uses Bayesian model selection to improve clustering quality. In this paper, we present an extension of the BHC algorithm. Our Gaussian BHC (GBHC) algorithm represents data as a mixture of Gaussian distributions. It use...

Clustering is one of the most widely used procedures in the analysis of microarray data, for example with the goal of discovering cancer subtypes based on observed heterogeneity of genetic marks between different tissues. It is wellknown that in such high-dimensional settings, the existence of many noise variables can overwhelm the few signals embedded in the high-dimensional space. We propose ...

Statistical clustering of criminal events can be used by crime analysts to create of lists of potential suspects for an unsolved crime, identify groups of crimes that may have been committed by the same individuals or group of individuals, for offender profiling, and for predicting future events. In this paper, we propose a Bayesian model-based clustering approach for criminal events. Our appro...