Experiments on Clustering Algorithms for Mixed and Incomplete Data

Abstract

Clustering mixed and incomplete data is a goal of frequent approaches in the last years because its common apparition in soft sciences problems. However, there is a lack of studies evaluating the performance of clustering algorithms for such kind of data. In this paper we present an experimental study about performance of seven clustering algorithms which used one of these techniques: partition, hierarchal or metaheuristic. All the methods ran over 15 databases from UCI Machine Learning Repository, having mixed and incomplete data descriptions. In external cluster validation using the indices Entropy and V-Measure, the algorithms that use the last technique showed the best results. Thus, we recommend metaheuristic based clustering algorithms for clustering data having mixed and incomplete descriptions.