Unsupervised ensemble minority clustering
Cluster a alysis 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...
| Autores: | , |
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| Tipo de recurso: | informe técnico |
| Fecha de publicación: | 2012 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/15664 |
| Acceso en línea: | https://hdl.handle.net/2117/15664 |
| Access Level: | acceso abierto |
| Palabra clave: | Clustering Clústers Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Llenguatge natural |
| Sumario: | Cluster a alysis 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 report, we present a novel ensemble minority clustering algorithm, Ewocs, suitable for weak clustering combination, and provide a theoretical proof of its properties under a loose set of constraints. The validity of the assumptions used in the proof is empirically assessed using a collection of synthetic datasets. |
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