A Hybrid Clustering Algorithm Based on Improved Artificial Fish Swarm

Liwei Tian, Lin Tian


K-medoids clustering algorithm is used to classify data, but the approach is sensitive to the initial selection of the centers and the divided cluster quality is not high. Basic Artificial Fish Swarm Algorithm is a new type of heuristic swarm intelligence algorithm, but optimization is difficult to get a very high precision due to the randomness of the artificial fish behavior. A novel clustering method based on improved global artificial fish swarm is proposed in this paper by analyzing the advantages and disadvantages of two algorithms, which has the ability to optimize the global clustering effect. The result of the experiment shows that quality of clustering is improved; the optimal central points and the clear division of data groups are obtained by the mathematical model combing improved fish swarm algorithm and K-medoids algorithm.


DOI : http://dx.doi.org/10.11591/telkomnika.v12i5.5110


Artificial Fish Swarm Algorithm; K-medoids algorithm; clustering analysis

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