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| Titel: |
Hybrid Self Organizing Map for Overlapping Clusters |
| Auteur: |
M.N.M. Sap Ehsan Mohebi |
| Verschenen in: |
International journal of signal processing, image processing and pattern recognition |
| Paginering: | Jaargang 1 (2008) nr. 1 pagina's 11-20 |
| Jaar: |
2008 |
| Inhoud: |
The Kohonen self organizing map is an excellent tool in exploratoryphase of data mining and pattern recognition. The SOM is a popular tool that maps high dimensional space into a small number of dimensions by placing similar elements close together, forming clusters. Recently researchers found that to capture the uncertainty involved in cluster analysis, it is not necessary to have crisp boundaries in some clustering operations. In this paper to overcomethe uncertainty, a two-level clustering algorithm based on SOM which employs the rough set theory is proposed. The two-level stage Rough SOM (first using SOM to produce the prototypes that are then clustered in the second stage) is found to perform well and more accurate compared with the proposed crisp clustering method (Incremental SOM) and reduces the errors. |
| Uitgever: |
SERSC (provided by DOAJ) |
| Bronbestand: |
Elektronische Wetenschappelijke Tijdschriften |
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