caa a22 4500 605478120 CHVBK 20210128100404.0 cr unu---uuuuu 210128e20150101xx s 000 0 eng 10.1007/s10994-013-5395-y doi (NATIONALLICENCE)springer-10.1007/s10994-013-5395-y Metacluster-based Projective Clustering Ensembles [Elektronische Daten] [Francesco Gullo, Carlotta Domeniconi, Andrea Tagarelli] The Projective Clustering Ensemble (PCE) problem is a recent clustering advance aimed at combining the two powerful tools of clustering ensembles and projective clustering. PCE has been formalized as either a two-objective or a single-objective optimization problem. Two-objective PCE has been recognized as more accurate than its single-objective counterpart, although it is unable to jointly handle the object-based and feature-based cluster representations. In this paper, we push forward the current PCE research, aiming to overcome the limitations of all existing PCE formulations. We propose a novel single-objective PCE formulation so that (i) the object-based and feature-based cluster representations are jointly considered, and (ii) the resulting optimization strategy follows a metacluster-based methodology borrowed from traditional clustering ensembles. As a result, the proposed formulation features best suitability to the PCE problem, thus guaranteeing improved effectiveness. Experiments on benchmark datasets have shown how the proposed approach achieves better average accuracy than all existing PCE methods, as well as efficiency superior to the most accurate existing metacluster-based PCE method on larger datasets. The Author(s), 2013 Clustering nationallicence Clustering ensembles nationallicence Projective clustering nationallicence Subspace clustering nationallicence Dimensionality reduction nationallicence Optimization nationallicence Gullo Francesco Yahoo! Research, 08018, Barcelona, Spain aut Domeniconi Carlotta Dept. of Computer Science, George Mason University, 22030, Fairfax, VA, USA aut Tagarelli Andrea DIMES Dept., University of Calabria, 87036, Rende, (CS), Italy aut Machine Learning Springer US; http://www.springer-ny.com 98/1-2(2015-01-01), 181-216 0885-6125 98:1-2<181 2015 98 10994 https://doi.org/10.1007/s10994-013-5395-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/s10994-013-5395-y text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Gullo Francesco Yahoo! Research, 08018, Barcelona, Spain aut NATIONALLICENCE 700 1- Domeniconi Carlotta Dept. of Computer Science, George Mason University, 22030, Fairfax, VA, USA aut NATIONALLICENCE 700 1- Tagarelli Andrea DIMES Dept., University of Calabria, 87036, Rende, (CS), Italy aut NATIONALLICENCE 773 0- Machine Learning Springer US; http://www.springer-ny.com 98/1-2(2015-01-01), 181-216 0885-6125 98:1-2<181 2015 98 10994