Convergence Analysis of Semi-supervised Clustering Ensemble

• DOI number:10.1109/ICIST.2013.6747660
• Affiliation of Author(s):西南交通大学
• Journal:2013 IEEE Third International Conference on Information Science and Technology (ICIST)
• Place of Publication:US
• Key Words:convergence learning (artificial intelligence pattern clustering convergence analysis ensemble learning semisupervised clustering ensemble semisupervised learning Convergence Diabetes Electronic mail
• Abstract:Semi-supervised clustering ensemble fully integrates the advantages of semi-supervised learning, clustering analysis and ensemble learning, as well as improves the performance of clustering. There are many works on the algorithm and the consensus function of semi-supervised clustering ensemble, but there are few studies in the theoretical analysis. In this paper, we analyze the convergence of semi-supervised clustering ensemble, and propose a new relabeling approach for semi-supervised clustering ensemble by majority voting. We prove that semi-supervised clustering ensemble is able to boost weak learners to strong learners which can make very accurate predictions. The experimental results on standard data sets show that the semi-supervised clustering ensemble has better performance.
• Co-author:Hongjun Wang, et al
• First Author:Dahai Chen
• Indexed by:Academic papers
• Correspondence Author:Yan Yang
• Document Code:20141817643152
• Discipline:Engineering
• First-Level Discipline:Computer Science and Technology
• Page Number:783-788
• ISSN No.:2164-4357
• Translation or Not:no
• Date of Publication:2013-05-23