A multiple k-means clustering ensemble algorithm to find nonlinearly separable clusters

Abstract Cluster ensemble is an important research content of ensemble learning, which is used to aggregate several base clusterings to generate a single output clustering with improved robustness and quality. Since clustering is unsupervised, where the “accuracy” does not have a clear meaning, most of existing ensemble methods try to obtain the most consistent clustering result with base clusterings. However, it is difficult for these methods to realize “Multi-weaks equal to a Strong”. For example, on a data set with nonlinearly separable clusters, if the base clusterings are produced by some linear clusterers, these methods generally cannot integrate them to obtain a good nonlinear clustering. In this paper, we select k-means as a base clusterer and provide an ensemble clusterer (algorithm) of multiple k-means clusterings based on a local hypothesis. In the new algorithm, we study the extraction of the local-credible labels from a base clustering, the production of different base clusterings, the construction of cluster relation and the final assignment of each object. The proposed ensemble clusterer not only inherits the scalability of k-means but also overcomes its limitation that it only can find linearly separable clusters. Finally, the experimental results illustrate its effectiveness and efficiency.