SOM variants for topological horizontal collaboration
| Autor: | Najet Arous, Ameni Filali, Chiraz Jlassi |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Linear programming
business.industry Computer science Correlation clustering 02 engineering and technology 010501 environmental sciences computer.software_genre Topology Machine learning 01 natural sciences Data visualization 0202 electrical engineering electronic engineering information engineering Feature (machine learning) FLAME clustering 020201 artificial intelligence & image processing Artificial intelligence Data mining business Cluster analysis Focus (optics) computer 0105 earth and related environmental sciences |
| Zdroj: | ATSIP |
| DOI: | 10.1109/atsip.2016.7523117 |
| Popis: | In this paper, we focus on collaborative clustering methods based on topological approaches, such as self-organizing maps (SOM) and self-organizing maps based on a locally adapting neighborhood radii (AdSOM). So far, the methods of clustering carried out on a single dataset, but current applications require datasets distributed among multiple sites. Thus, the communication between various datasets is required. The basic concept of collaborative clustering is to collaborate by exchanging information on their consensus. The strength of collaboration, or confidence, is specified by a coefficient of collaboration. Collaboration can be made vertical, horizontal and hybrid: The horizontal collaboration is applied for datasets that represent the same individuals but with different feature spaces. However, the vertical collaboration occurs when the datasets contain different individuals and same variables. Hybrid collaboration allows combining the horizontal and the vertical approaches. In this present work, we are particularly interested in horizontal topological collaborative clustering. |
| Databáze: | OpenAIRE |
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