Fuzzy c-varieties/elliptotypes clustering in reproducing kernel Hilbert space
✍ Scribed by Jacek M. Łęski
- Publisher
- Elsevier Science
- Year
- 2004
- Tongue
- English
- Weight
- 374 KB
- Volume
- 141
- Category
- Article
- ISSN
- 0165-0114
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✦ Synopsis
Fuzzy clustering algorithms are successfully applied to a wide variety of problems, such as: pattern recognition, image analysis, modeling and so on. The Fuzzy C-M eans (FCM) method is one of the most popular clustering methods based on the minimization of a criterion function. However, the performance of the FCM method is good only when a data set contains clusters that are approximately the same size and shape. In this paper, a simple idea will be used, to overcome this problem. The original input (data) space will be mapped into the high (possibly inÿnite)-dimensional feature space F through some nonlinear mapping. In this space the data structures will be modeled by the linear varieties or elliptotypes. This method is called Kernel Fuzzy C-V arieties/Elliptotypes clustering algorithm. Performance of the new clustering algorithm is experimentally compared with FCM and fuzzy c-varieties/elliptotypes methods using synthetic datasets and real-life datasets.