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Comparison of sga and rga based clustering algorithm for pattern recognition.
| Content Provider | CiteSeerX |
|---|---|
| Author | Dhiraj, Kumar Rath, Santanu Kumar |
| Abstract | Abstract--- In this paper Genetic Algorithm based clustering Algorithm has been studied for pattern recognition. The searching capability of genetic algorithms is exploited in order to search for appropriate/optimal cluster as well as cluster’s center in the feature space such that inter-cluster distance (Homogeneity) and intra-cluster distances (Separation) are optimized. We use H-S ratio for computation of fitness function. We use Anderson’s IRIS data to illustrate our method. We have implemented six clustering algorithm (k-means, Hierarchical, GLVQ, SOM, FCM and GA-based clustering algorithm) and compare clustering accuracy using IRIS data. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Pattern Recognition Clustering Algorithm Iris Data Feature Space Inter-cluster Distance Cluster Center Fitness Function Genetic Algorithm Paper Genetic Algorithm H-s Ratio Ga-based Clustering Algorithm Intra-cluster Distance Appropriate Optimal Cluster |
| Content Type | Text |
| Resource Type | Article |
