Semi-supervised clustering ensemble based on multi-ant colonies algorithm

• Affiliation of Author(s):西南交通大学
• Journal:7th International Conference on Rough Sets and Knowledge Technology
• Key Words:semi-supervised clustering, clustering ensemble, multi-ant colonies clustering, pairwise constraints.
• Abstract:Semi-supervised clustering ensemble has emerged as an important elaboration of classical clustering problem that improves quality and robustness in clustering by combining the results of different clustering components with user provided constraints. In this paper, we propose a novel semi-supervised consensus clustering algorithm based on multiant colonies. Our method incorporates pairwise constraints not only in each ant colony clustering process, but also in computing new similarity matrix during the multi-ant colonies ensemble. Experimental results demonstrate the effectiveness of the proposed method.
• Co-author:Hongjun Wang, Chao Lin,Jinyuan Zhang
• First Author:Yan Yang
• Indexed by:Academic papers
• Correspondence Author:T. Li
• Document Code:20123415353933
• Discipline:Engineering
• First-Level Discipline:Computer Science and Technology
• Issue:RSKT 2012, LNAI 7414
• Page Number:pp. 302–309, 2012
• Translation or Not:no