Metacluster-based Projective Clustering Ensembles
| LEADER | caa a22 4500 | ||
|---|---|---|---|
| 001 | 605478120 | ||
| 003 | CHVBK | ||
| 005 | 20210128100404.0 | ||
| 007 | cr unu---uuuuu | ||
| 008 | 210128e20150101xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.1007/s10994-013-5395-y |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s10994-013-5395-y | ||
| 245 | 0 | 0 | |a Metacluster-based Projective Clustering Ensembles |h [Elektronische Daten] |c [Francesco Gullo, Carlotta Domeniconi, Andrea Tagarelli] |
| 520 | 3 | |a 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. | |
| 540 | |a The Author(s), 2013 | ||
| 690 | 7 | |a Clustering |2 nationallicence | |
| 690 | 7 | |a Clustering ensembles |2 nationallicence | |
| 690 | 7 | |a Projective clustering |2 nationallicence | |
| 690 | 7 | |a Subspace clustering |2 nationallicence | |
| 690 | 7 | |a Dimensionality reduction |2 nationallicence | |
| 690 | 7 | |a Optimization |2 nationallicence | |
| 700 | 1 | |a Gullo |D Francesco |u Yahoo! Research, 08018, Barcelona, Spain |4 aut | |
| 700 | 1 | |a Domeniconi |D Carlotta |u Dept. of Computer Science, George Mason University, 22030, Fairfax, VA, USA |4 aut | |
| 700 | 1 | |a Tagarelli |D Andrea |u DIMES Dept., University of Calabria, 87036, Rende, (CS), Italy |4 aut | |
| 773 | 0 | |t Machine Learning |d Springer US; http://www.springer-ny.com |g 98/1-2(2015-01-01), 181-216 |x 0885-6125 |q 98:1-2<181 |1 2015 |2 98 |o 10994 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s10994-013-5395-y |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s10994-013-5395-y |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Gullo |D Francesco |u Yahoo! Research, 08018, Barcelona, Spain |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Domeniconi |D Carlotta |u Dept. of Computer Science, George Mason University, 22030, Fairfax, VA, USA |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Tagarelli |D Andrea |u DIMES Dept., University of Calabria, 87036, Rende, (CS), Italy |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Machine Learning |d Springer US; http://www.springer-ny.com |g 98/1-2(2015-01-01), 181-216 |x 0885-6125 |q 98:1-2<181 |1 2015 |2 98 |o 10994 | ||