Affiliations: [a] School of Computer Science and Cyberspace Security, Hainan University, China
| [b] Institute of Research and Development, Duy Tan University, Da Nang, Vietnam | [c] Faculty of Information Technology, Duy Tan University, Da Nang, Vietnam | [d] Department of Computer Science, Nourabad Mamasani Branch, Islamic Azad University, Mamasani, Iran
| [e] Computer Science Department, College of Computer and Information Sciences, AI Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
| [f] Computer Science Department, Faculty of Applied Science, Taiz University, Taiz, Yemen
| [g] Department of Mathematics Education, Teachers College, Can Tho University, Can Tho City, Vietnam | [h] Fractional Calculus, Optimization and Algebra Research Group, Faculty of Mathematics and Statistics, Ton Duc Thang University, Ho Chi Minh City, Vietnam
Abstract: A bad partition in an ensemble will be removed by a cluster ensemble selection framework from the final ensemble. It is the main idea in cluster ensemble selection to remove these partitions (bad partitions) from the selected ensemble. But still, it is likely that one of them contains some reliable clusters. Therefore, it may be reasonable to apply the selection phase on cluster level. To do this, a cluster evaluation metric is needed. Some of these metrics have been recently introduced; each of them has its limitations. The weak points of each method have been addressed in the paper. Subsequently, a new metric for cluster assessment has been introduced. The new measure is named Balanced Normalized Mutual Information (BNMI) criterion. It balances the deficiency of the traditional NMI-based criteria. Additionally, an innovative cluster ensemble approach has been proposed. To create the consensus partition considering the elected clusters, a set of different aggregation-functions (called also consensus-functions) have been utilized: the ones which are based upon the co-association matrix (CAM), the ones which are based on hyper graph partitioning algorithms, and the ones which are based upon intermediate space. The experimental study indicates that the state-of-the-art cluster ensemble methods are outperformed by the proposed cluster ensemble approach.
