A review of conceptual clustering algorithms

Clustering is a fundamental technique in data mining and pattern recognition, which has been successfully applied in several contexts. However, most of the clustering algorithms developed so far have been focused only in organizing the collection of objects into a set of clusters, leaving the interpretation of those clusters to the user. Conceptual clustering algorithms, in addition to the list of objects belonging to the clusters, provide for each cluster one or several concepts, as an explanation of the clusters. In this work, we present an overview of the most influential algorithms reported in the field of conceptual clustering, highlighting their limitations or drawbacks. Additionally, we present a taxonomy of these methods as well as a qualitative comparison of these algorithms, regarding a set of characteristics desirable since a practical point of view, which may help in the selection of the most appropriate method for solving a problem at hand. Finally, some research lines that need to be further developed in the context of conceptual clustering are discussed.