In the last decades, a wide portfolio of Feature Weighting (FW) methods have been
proposed in the literature. Their main potential is the capability to transform the features in …

Knowing the number of clusters a priori is one of the most challenging aspects of
unsupervised learning. Clustering Internal Validity Indices (CIVIs) evaluate partitions in …

The aim of collaborative clustering is to make different clustering methods collaborate, in
order to reach at an agreement on the partitioning of a common dataset. As different …

Clustering is an unsupervised process which aims to discover regularities and underlying
structures in data. Constrained clustering extends clustering in such a way that expert …

Constrained clustering is becoming an increasingly popular approach in data mining. It
offers a balance between the complexity of producing a formal definition of thematic classes …

The extracellular action potentials recorded on an electrode result from the collective
simultaneous electrophysiological activity of an unknown number of neurons. Identifying and …

Massive data can create a real competitive advantage for the companies; it is used to better
respond to customers, to follow the behavior of consumers, to anticipate the evolutions, etc …

In this paper, we consider the problem of distributed unsupervised learning where data to be
clustered are partitioned over a set of agents having limited connectivity. In order to solve …

High-throughput approaches in computational materials discovery often yield a
combinatorial explosion that makes the exhaustive rendering of complete structural and …

This article proposes a semi-interactive system for visual data exploration using an iterative
clustering that combines an automatic approach with an interactive one. We propose a …