In previous work, we have proposed a novel approach to data clustering based on the explicit optimization of a partitioning with respect to two complementary clustering objectives ...
We present in this paper a modification of Lumer and Faieta’s algorithm for data clustering. This algorithm discovers automatically clusters in numerical data without prior kno...
Recent advances in data clustering concern clustering ensembles and projective clustering methods, each addressing different issues in clustering problems. In this paper, we consi...
Francesco Gullo, Carlotta Domeniconi, Andrea Tagar...
Motivated by the principle of agnostic learning, we present an extension of the model introduced by Balcan, Blum, and Gupta [3] on computing low-error clusterings. The extended mod...
Data clustering is an important task in many disciplines. A large number of studies have attempted to improve clustering by using the side information that is often encoded as pai...