DOI: 10.3724/SP.J.1187.2012.00800

Journal of Electronic Measurement and Instrument (电子测量与仪器学报) 2012/26:9 PP.800-804

Clustering algorithm based on fusion of ant colony algorithm and K-medoids

Ant colony algorithm can achieve autonomous clustering without any prior knowledge and human intervention. It is strong robust and easy to combine with other algorithms. But ant colony algorithm is expensive on time consuming. K-medoids algorithm is a classical clustering algorithm based on partitioning. It is widely used because it has high speed and good efficiency. But the number of clusters must be prior decided. K-medoids algorithm dependents on the initial cluster centre points. In order to resolve these problems, a clustering algorithm named ant colony algorithm and K-medoids clustering algorithm (AKCA) is proposed. The advantage of ant colony algorithm is incorporated with K-medoids algorithm. The experimental results show that the proposed algorithm has high efficiency, clustering quality and adaptability for small scale databases.

Key words:cluster analysis,ant colony algorithm,K-medoids algorithm

ReleaseDate:2014-07-21 16:20:59

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