Hybrid Genetic Clustering by Using FCM and Geodesic Distance for Complex Distributed Data
| Autor: | Yong Sheng Yang, Gang Li, Yong Sheng Zhu, You Yun Zhang |
|---|---|
| Rok vydání: | 2012 |
| Předmět: |
Fuzzy clustering
business.industry Single-linkage clustering Correlation clustering Pattern recognition General Medicine Hierarchical clustering ComputingMethodologies_PATTERNRECOGNITION Data stream clustering CURE data clustering algorithm Artificial intelligence business Cluster analysis k-medians clustering Mathematics |
| Zdroj: | Applied Mechanics and Materials. :2597-2601 |
| ISSN: | 1662-7482 |
| DOI: | 10.4028/www.scientific.net/amm.263-266.2597 |
| Popis: | To efficiently find hidden clusters in datasets with complex distributed data，inspired by complementary strategies, a hybrid genetic clustering algorithm was developed, which is on the basis of the geodesic distance metric, and combined with the Fuzzy C-Means clustering (FCM) algorithm. First, instead of using Euclidean distance，the new approach employs geodesic distance based dissimilarity metric during all fitness evaluation. And then, with the help of FCM clustering, some sub-clusters with spherical distribution are partitioned effectively. Next, a genetic algorithm based clustering using geodesic distance metric, named GCGD, is adopted to cluster the clustering centers obtained from FCM clustering. Finally, the final results are acquired based on above two clustering results. Experimental results on eight benchmark datasets clustering questions show the effectiveness of the algorithm as a clustering technique. Compared with conventional GCGD, the hybrid clustering can decrease the computational time obviously, while retaining high clustering correct ratio. |
| Databáze: | OpenAIRE |
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