Hybrid Clustering Using ACO and AHC

doi:10.13190/jbupt.201303.61.xionggw

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2013, Vol. 36 ›› Issue (3): 60-63,78.doi: 10.13190/jbupt.201303.61.xionggw

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Hybrid Clustering Using ACO and AHC

XIONG Wen, JIN Yao-hong

1. Institute of Chinese Information Processing, Beijing Normal University, Beijing 100875, China;
2. CPIC-BNU Joint Laboratory of Machine Translation, Beijing 100875, China

Received:2012-07-23 Online:2013-06-30 Published:2013-06-30

Abstract

Abstract:

To study the use of intelligent ant-colony optimization (ACO) to improve agglomerative hierarchical clustering (AHC) and to attain high-quality cluster results of hierarchy, a hybrid clustering based on ACO and AHC (HCAA) is proposed. The modified AHC and a new objective function are used to generate the dendrogram of clusters and the internal index is utilized to evaluate the solution. The mechanism of pheromone feedback and pheromone volatilization supported by the ACO is employed to control the search of the ant colony in the solution space. The method will accelerate the search, avoiding the results of local optima because of using meta-heuristic optimization. Experiments on several datasets of university of California, Irvine verify the feasibility of this method.

Key words: artificial intelligence, ant colony optimization, data mining, agglomerative hierarchical clustering

CLC Number:

TP274
TP181

XIONG Wen, JIN Yao-hong. Hybrid Clustering Using ACO and AHC[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2013, 36(3): 60-63,78.

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Hybrid Clustering Using ACO and AHC

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