This paper proposes a tentative and original survey of meeting points between Knowledge
Representation and Reasoning (KRR) and Machine Learning (ML), two areas which have …

Constrained Clustering allows to make the clustering task more accurate by integrating user
constraints, which can be instance-level or cluster-level constraints. Few works consider the …

Constrained clustering extends clustering by integrating user constraints, and aims to
determine an optimal assignment under the constraints. In this paper, we propose a local …

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 …

Conceptual clustering combines two long-standing machine learning tasks: the
unsupervised grou** of similar instances and their description by symbolic concepts. In …

Clustering is an important Machine Learning task, which aims at discovering the implicit
structure of data. Applying a clustering algorithm is easy but since clustering is an …

The minimum sum-of-squares clustering (MSSC), or k-means type clustering, is traditionally
considered an unsupervised learning task. In recent years, the use of background …

This paper provides an overview of the current state-of-the-art on using constraints in
knowledge discovery and data mining. The use of constraints in a data mining task requires …