Robust Extension of FCM Algorithm
| Autor: | Cheng-Jia Li |
|---|---|
| Rok vydání: | 2006 |
| Předmět: |
Fuzzy clustering
Mathematics::General Mathematics business.industry Fuzzy set Correlation clustering ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition Fuzzy logic ComputingMethodologies_PATTERNRECOGNITION CURE data clustering algorithm Canopy clustering algorithm FLAME clustering Artificial intelligence Cluster analysis business Algorithm Mathematics |
| Zdroj: | 2006 International Conference on Machine Learning and Cybernetics. |
| DOI: | 10.1109/icmlc.2006.258710 |
| Popis: | Clustering is a procedure through which objects are distinguished or classified in accordance with their similarity. The fuzzy c-means method (FCM) is one of the most popular clustering methods based on minimization of a criterion function. However, the FCM method is sensitive to the presence of noise and outliers in data. A new clustering algorithm is proposed by extending the criterion function, which includes the well-known fuzzy c-means method as its special case. Numerical experiments show that the new clustering algorithm is less sensitive than the traditional FCM method and robust to outliers. |
| Databáze: | OpenAIRE |
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