Markov clustering ensemble

Abstract

Clustering ensemble is an unsupervised ensemble learning method that is very important in machine learning, since it integrates multiple weak base clustering results to produce a strong consistency result. This paper proposes the Markov clustering ensemble (MCE) model to solve the weak stability and robustness of soft clustering ensemble. First, the base clustering algorithms are regarded as new features of the original datasets. Then, the results of the base clustering algorithms are the values of these features, which can break through the framework of consensus cluster ensemble. Second, as the base clustering results are discrete data, the maximum information coefficient is applied to measure their similarity. Accordingly, a graph-based cluster ensemble model can be constructed with row vectors as vertices and the similarity between row vectors as edges. Then, the Markov process can be applied to infer the graph-based cluster ensemble model; therefore, the MCE algorithm is designed according to the inference. To test the performance of the MCE algorithm, clustering algorithms and clustering ensemble algorithms are used to conduct comparative experiments on ten datasets. The experimental results show that the MCE algorithm outperforms the other algorithms in terms of accuracy and purity.