Constraint-based clustering algorithms exploit background knowledge to construct clusterings that are aligned with the interests of a particular user. This background knowledge is often obtained by allowing the clustering system to pose pairwise queries to the user: should these two elements be in the same cluster or not? Active clustering methods aim to minimize the number of queries needed to...

It is widely reported in the literature that incremental clustering systems suuer from instance ordering eeects and that under some orderings, extremely poor clusterings may be obtained. In this paper we present a new strategy aimed to mitigate these eeects, the Not-Yet strategy which has a general and open formulation and it is not coupled to any particular system. Results suggest that the str...

This paper considers the parameter estimation problem in Stochastic Block Model with Overlaps (SBMO), which is a quite general instance of random graph model allowing for overlapping community structure. We present the new algorithm successive projection overlapping clustering (SPOC) which combines the ideas of spectral clustering and geometric approach for separable non-negative matrix factori...

In this paper, we provide an approach to clustering relational matrices whose entries correspond to either similarities or dissimilarities between objects. Our approach is based on the value of information, a parameterized, information-theoretic criterion that measures the change in costs associated with changes in information. Optimizing the value of information yields a deterministic annealin...

EvIdent (EVent IDENTification) is a user-friendly, algorithm-rich, exploratory data analysis software for quickly detecting, investigating, and visualizing novel events in a set of images as they evolve in time and/or frequency. For instance, in a series of functional magnetic resonance neuroimages, novelty may manifest itself as neural activations in a time course. The core of the system is an...

Low-dimensional embedding, manifold learning, clustering, classification, and anomaly detection are among the most important problems in machine learning. The existing methods usually consider the case when each instance has a fixed, finite-dimensional feature representation. We wish to expand the domain of consideration and let each instance correspond to a continuous probability distribution ...

In this paper we present a novel Incremental Hierarchical Clustering (IHC) algorithm. Our approach aims to construct a hierarchy that satisfies the homogeneity and the monotonicity properties. Working in a bottom-up fashion, a new instance is placed in the hierarchy and a sequence of hierarchy restructuring process is performed only in regions that have been affected by the presence of the new ...

We present an improved method for clustering in the presence of very limited supervisory information, given as pairwise instance constraints. By allowing instance-level constraints to have spacelevel inductive implications, we are able to successfully incorporate constraints for a wide range of data set types. Our method greatly improves on the previously studied constrained -means algorithm, g...

The advent of fast computer systems has enabled scientists to visualize and analyze complex phenomena (such as explosions of stars and expressions of genes) [2][3][7][8][9][10][12][13]. These complex phenomena (whether simulated or observed) generate large-scale data sets. For instance, simulations of supernovae easily produce terabytes of data [1]. Given such massive amounts of data, it is not...