Unsupervised ensemble minority clustering
| LEADER | caa a22 4500 | ||
|---|---|---|---|
| 001 | 605478082 | ||
| 003 | CHVBK | ||
| 005 | 20210128100404.0 | ||
| 007 | cr unu---uuuuu | ||
| 008 | 210128e20150101xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.1007/s10994-013-5394-z |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s10994-013-5394-z | ||
| 245 | 0 | 0 | |a Unsupervised ensemble minority clustering |h [Elektronische Daten] |c [Edgar Gonzàlez, Jordi Turmo] |
| 520 | 3 | |a Cluster analysis lies at the core of most unsupervised learning tasks. However, the majority of clustering algorithms depend on the all-in assumption, in which all objects belong to some cluster, and perform poorly on minority clustering tasks, in which a small fraction of signal data stands against a majority of noise. The approaches proposed so far for minority clustering are supervised: they require the number and distribution of the foreground and background clusters. In supervised learning and all-in clustering, combination methods have been successfully applied to obtain distribution-free learners, even from the output of weak individual algorithms. In this work, we propose a novel ensemble minority clustering algorithm, Ewocs, suitable for weak clustering combination. Its properties have been theoretically proved under a loose set of constraints. We also propose a number of weak clustering algorithms, and an unsupervised procedure to determine the scaling parameters for Gaussian kernels used within the task. We have implemented a number of approaches built from the proposed components, and evaluated them on a collection of datasets. The results show how approaches based on Ewocs are competitive with respect to—and even outperform—other minority clustering approaches in the state of the art. | |
| 540 | |a The Author(s), 2013 | ||
| 690 | 7 | |a Clustering |2 nationallicence | |
| 690 | 7 | |a Minority clustering |2 nationallicence | |
| 690 | 7 | |a Ensemble clustering |2 nationallicence | |
| 690 | 7 | |a Weak learning |2 nationallicence | |
| 700 | 1 | |a Gonzàlez |D Edgar |u TALP Research Center, Universitat Politècnica de Catalunya, c/Jordi Girona, 1, 08034, Barcelona, Spain |4 aut | |
| 700 | 1 | |a Turmo |D Jordi |u TALP Research Center, Universitat Politècnica de Catalunya, c/Jordi Girona, 1, 08034, Barcelona, Spain |4 aut | |
| 773 | 0 | |t Machine Learning |d Springer US; http://www.springer-ny.com |g 98/1-2(2015-01-01), 217-268 |x 0885-6125 |q 98:1-2<217 |1 2015 |2 98 |o 10994 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s10994-013-5394-z |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/s10994-013-5394-z |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Gonzàlez |D Edgar |u TALP Research Center, Universitat Politècnica de Catalunya, c/Jordi Girona, 1, 08034, Barcelona, Spain |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Turmo |D Jordi |u TALP Research Center, Universitat Politècnica de Catalunya, c/Jordi Girona, 1, 08034, Barcelona, Spain |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), 217-268 |x 0885-6125 |q 98:1-2<217 |1 2015 |2 98 |o 10994 | ||