caa a22 4500 605478082 CHVBK 20210128100404.0 cr unu---uuuuu 210128e20150101xx s 000 0 eng 10.1007/s10994-013-5394-z doi (NATIONALLICENCE)springer-10.1007/s10994-013-5394-z Unsupervised ensemble minority clustering [Elektronische Daten] [Edgar Gonzàlez, Jordi Turmo] 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. The Author(s), 2013 Clustering nationallicence Minority clustering nationallicence Ensemble clustering nationallicence Weak learning nationallicence Gonzàlez Edgar TALP Research Center, Universitat Politècnica de Catalunya, c/Jordi Girona, 1, 08034, Barcelona, Spain aut Turmo Jordi TALP Research Center, Universitat Politècnica de Catalunya, c/Jordi Girona, 1, 08034, Barcelona, Spain aut Machine Learning Springer US; http://www.springer-ny.com 98/1-2(2015-01-01), 217-268 0885-6125 98:1-2<217 2015 98 10994 https://doi.org/10.1007/s10994-013-5394-z 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/s10994-013-5394-z text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Gonzàlez Edgar TALP Research Center, Universitat Politècnica de Catalunya, c/Jordi Girona, 1, 08034, Barcelona, Spain aut NATIONALLICENCE 700 1- Turmo Jordi TALP Research Center, Universitat Politècnica de Catalunya, c/Jordi Girona, 1, 08034, Barcelona, Spain aut NATIONALLICENCE 773 0- Machine Learning Springer US; http://www.springer-ny.com 98/1-2(2015-01-01), 217-268 0885-6125 98:1-2<217 2015 98 10994