This work examines under what conditions compression methodologies can retain the outcome of clustering operations. We focus on the popular k-Means clustering algorithm and we dem...
Deepak S. Turaga, Michail Vlachos, Olivier Versche...
This paper introduces a novel statistical mixture model for probabilistic grouping of distributional histogram data. Adopting the Bayesian framework, we propose to perform anneale...
Abstract. We consider the problem of finding communities in large linked networks such as web structures or citation networks. We review similarity measures for linked objects and...
The scope of the well-known k-means algorithm has been
broadly extended with some recent results: first, the k-
means++ initialization method gives some approximation
guarantees...
Temporal Clustering (TC) refers to the factorization of multiple time series into a set of non-overlapping segments that belong to k temporal clusters. Existing methods based on e...