Evolutionary Clustering Algorithm Using Supervised Classifiers

Cluster analysis is a data mining tool for searching patterns automatically on different types of data. However, it is not always clear which clustering criterion would be the most accurate as this decision is domain-dependent. This paper focuses on the design of a single-objective evolutionary clustering algorithm that generates solutions that are less biased towards one cluster structure. This work's motivation starts from the idea that a good partition induces a well-trained classifier. The resulting evolutionary clustering algorithm using classifiers aims to enhance the generalization capability of the resulting partition. The proposed objective function trains a set of classifiers using an individual's chromosome as class labels. The obtained average area under the curve of the classifiers is used as the cluster quality index for measuring fitness. The experimental phase shows a similarity increase between the generated solutions and the reference partitions of the datasets.