We discuss the design of a tool for the interactive exploration of part-of-speech classes using structural features. At the heart of the tool are incremental hierarchical clustering algorithms. The algorithms are used to detect classes using morphological and syntactical features. The algorithms have been modified or designed to allow interactive exploration and constrained clustering. We prese...

In this paper, an on-line, dynamic clustering method — ECM, Evolving Clustering Method, is proposed. The ECM is usually used for on-line systems, in which it performs an one-pass, maximum distance-based clustering process without any optimisation. The ECM can also be applied to off-line problems where a constrained minimisation is introduced.

The problems of finding alternative clusterings and avoiding bias have gained popularity over the last years. In this paper we put the focus on the quality of these alternative clusterings, proposing two approaches based in the use of negative constraints in conjunction with spectral clustering techniques. The first approach tries to introduce these constraints in the core of the constrained no...

The (constrained) minimization of a ratio of set functions is a problem frequently occurring in clustering and community detection. As these optimization problems are typically NP-hard, one uses convex or spectral relaxations in practice. While these relaxations can be solved globally optimally, they are often too loose and thus lead to results far away from the optimum. In this paper we show t...

This paper introduces a new method of fuzzy semisupervised hierarchical clustering using fuzzy instance level constraints. It introduces the concepts of fuzzy must-link and fuzzy cannot-link constraints and use them to find the optimum α-cut of a dendrogram. This method is used to approach the problem of classifying scientific publications in web digital libraries. It is tested on real data fro...