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Application of ant K-means on clustering analysis

✍ Scribed by R.J. Kuo; H.S. Wang; Tung-Lai Hu; S.H. Chou

Book ID
104007669
Publisher
Elsevier Science
Year
2005
Tongue
English
Weight
902 KB
Volume
50
Category
Article
ISSN
0898-1221

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✦ Synopsis

This paper intends to propose a novel clustering method, ant K-means (AK) algorithm. AK algorithm modifies the K-means as locating the objects in a cluster with the probability, which is updated by the pheromone, while the rule of updating pheromone is according to total within cluster variance (TWCV). The computational results showed that it is better than the other two methods, self-organizing feature map (SOM) followed by K-means method and SOM followed by genetic Kmeans algorithm via 243 data sets generated by Monte Carlo simulation. To further testify this novel method, the questionnaire survey data for the plasma television market segmentation is employed. The results also indicated that the proposed method is the best among these three methods based on TWCV. @ 2005 Elsevier Ltd. All rights reserved.

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