caa a22 4500 605478066 CHVBK 20210128100404.0 cr unu---uuuuu 210128e20150101xx s 000 0 eng 10.1007/s10994-013-5342-y doi (NATIONALLICENCE)springer-10.1007/s10994-013-5342-y Comparative study of matrix refinement approaches for ensemble clustering [Elektronische Daten] [Natthakan Iam-On, Tossapon Boongoen] Cluster ensembles or consensus clusterings have been shown to be better than any standard clustering algorithm at improving accuracy and robustness across various sets of data. This meta-learning formalism also helps users to overcome the dilemma of selecting an appropriate technique and the parameters for that technique. Since founded, different research areas have emerged with the common purpose of enhancing the effectiveness and applicability of cluster ensembles. These include the selection of ensemble members, the imputation of missing values, and the summarization of ensemble members. In particular, this paper is set to provide the review of different matrix refinement approaches that have been recently proposed in the literature for summarizing information of multiple clusterings. With various benchmark datasets and quality measures, the comparative study of these novel techniques is carried out to provide empirical findings from which a practical guideline can be drawn. The Author(s), 2013 Cluster ensemble nationallicence Multiple clusterings nationallicence Summarization nationallicence Information matrix nationallicence Iam-On Natthakan School of Information Technology, Mae Fah Luang University, 57100, Chiang Rai, Thailand aut Boongoen Tossapon Department of Mathematics and Computer Science, Royal Thai Air Force Academy, 10220, Bangkok, Thailand aut Machine Learning Springer US; http://www.springer-ny.com 98/1-2(2015-01-01), 269-300 0885-6125 98:1-2<269 2015 98 10994 https://doi.org/10.1007/s10994-013-5342-y text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10994-013-5342-y text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Iam-On Natthakan School of Information Technology, Mae Fah Luang University, 57100, Chiang Rai, Thailand aut NATIONALLICENCE 700 1- Boongoen Tossapon Department of Mathematics and Computer Science, Royal Thai Air Force Academy, 10220, Bangkok, Thailand aut NATIONALLICENCE 773 0- Machine Learning Springer US; http://www.springer-ny.com 98/1-2(2015-01-01), 269-300 0885-6125 98:1-2<269 2015 98 10994