Robust adaptive genetic K-Means algorithm using greedy selection for clustering
| Autor: | Abba Suganda Girsang, Fidelson Tanzil, Yogi Udjaja |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
0209 industrial biotechnology
Fuzzy clustering Single-linkage clustering Correlation clustering Constrained clustering 02 engineering and technology computer.software_genre 020901 industrial engineering & automation CURE data clustering algorithm 0202 electrical engineering electronic engineering information engineering Canopy clustering algorithm 020201 artificial intelligence & image processing Data mining Cluster analysis computer Algorithm k-medians clustering Mathematics |
| Zdroj: | KICSS |
| DOI: | 10.1109/kicss.2016.7951445 |
| Popis: | Clustering is a task to divide objects into group depends on their similarity. The optimal of solving clustering problem occurs when the data joins in one group which has a similar category. This study combines Adaptive Genetic Algorithm, K-Means and Greedy Selection to solve clustering problem, named RAGKA. In first step, the centroid is determined by K-Means. Crossover and mutation are performed based on the fitness value of each centroid. At last, the greedy search is operated to get the better solution. To show the performance of RAGKA, five data sets of clustering problem are used. Moreover, RAGKA is compared with other methods as well. The result shows that RAGKA is successfully to solve cluster problem and outperforms than the others. |
| Databáze: | OpenAIRE |
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