Nonnegative matrix factorization for clustering ensemble based on dark knowledge

• Impact Factor:8.139
• DOI number:10.1016/j.knosys.2018.09.021
• Affiliation of Author(s):西南交通大学
• Journal:KNOWLEDGE-BASED SYSTEMS
• Place of Publication:NETHERLANDS
• Key Words:Cluster ensembleNonnegative matrix factorizationDark knowledge
• Abstract:Traditional cluster ensemble (CE) methods use labels produced by base learning algorithms to obtain an ensemble result. These base learning algorithms can also obtain other information, such as parameter, covariance, or probability data, which is called dark knowledge. In this paper, we propose a method for integrating dark knowledge, which is usually ignored, into the ensemble learning process. This provides more information about the base clustering. We apply nonnegative matrix factorization (NMF) to the clustering ensemble model based on dark knowledge. First, different base clustering results are obtained by using various clustering configurations, before dark knowledge of every base clustering algorithm is extracted. NMF is then applied to the dark knowledge to obtain integrated results. Experimental results show that the method outperforms other clustering ensemble techniques.
• Co-author:ShanYan,Tianrui Li, YanYang
• First Author:WentingYe
• Indexed by:Academic papers
• Correspondence Author:HongjunWang
• Document Code:20184506042825
• Discipline:Engineering
• First-Level Discipline:Computer Science and Technology
• Volume:Volume 163,
• Issue:1 January 2019,
• Page Number:Pages 624-631
• ISSN No.:0950-7051
• Translation or Not:no
• Date of Publication:2018-11-01