Cascade Evaluation of Clustering Algorithms

Abstract

This paper is about the evaluation of the results of clustering algorithms, and the comparison of such algorithms. We propose a new method based on the enrichment of a set of independent labeled datasets by the results of clustering, and the use of a supervised method to evaluate the interest of adding such new information to the datasets. We thus adapt the cascade generalization [1] paradigm in the case where we combine an unsupervised and a supervised learner. We also consider the case where independent supervised learnings are performed on the different groups of data objects created by the clustering [2]. We then conduct experiments using different supervised algorithms to compare various clustering algorithms. And we thus show that our proposed method exhibits a coherent behavior, pointing out, for example, that the algorithms based on the use of complex probabilistic models outperform algorithms based on the use of simpler models.