The correlation clustering problem identifies clusters in a set of objects when the qualitative
information about objects' mutual similarities or dissimilarities is given in a signed network …

Pawlak's indiscernibility relation (which is an equivalence relation) represents a limit of our
knowledge embedded in an information system. In many cases covering approximation …

Correlation clustering relies on a relation of similarity (and the generated cost function). If the
similarity relation is a tolerance relation, then not only one optimal partition may exist: an …

In this article we propose a two-step classification method. At the first step it constructs a
tolerance relation from the data, and at second step it uses correlation clustering to construct …

In this article we propose two effective methods to produce a near optimal solution for the
problem of correlation clustering. We study their properties at different circumstances, and …

Pawlakian spaces rely on an equivalence relation which represent indiscernibility. As a
generalization of these spaces, some approximation spaces have appeared that are not …

We use representatives to reduce complexity in many areas of life. Clusters are often
replaced with their centre, and then these representatives are used to classify new objects. If …

The Correlation Clustering is a classical, NP hard optimization problem with many social,
economic, physical, biological and computer science applications. We had implemented …

Previously we generated tolerance relations from the distance of objects. In this paper the
situation is reversed: from any tolerance relation we generate a 2D representation in a such …

The main result of my research was developing a completely new approximation space
which was based on a tolerance relation (based on similarity). In this space, the system of …