Analysis of diversity measures in clustering ensembles

The diversity of an ensemble is known to be an important factor in determining its performance. There are a number of ways to quantify diversity in ensembles of classifiers, while little research has been done in clustering ensembles. This paper compares seven diversity measures of clustering ensembles with regard to their possible use in ensemble design. Five experiments have been designed to examine the relationships between the accuracy of the clustering ensembles and the measures of diversity under conditions of difference ensemble methods, different ensemble size and different data distributions respectively. Experiments show the relationships between these diversity measures and ensemble performances are not monotonous. However, when constructing ensembles with moderate ensemble size by suitable clustering algorithms for a given data set with uniform cluster distribution, the correlation coefficients between the diversity measures and ensemble performances are relatively high. Finally, the authors give some useful suggestions about the usefulness of diversity measures in building clustering ensembles.