A Novel Hybrid Data Clustering Algorithm Based on Artificial Bee Colony Algorithm and K-Means

To improve the performance of K-means clustering algorithm, this paper presents a new hybrid approach of Enhanced artificial bee colony algorithm and Kmeans(EABCK). In EABCK, the original artificial bee colony algorithm(called ABC) is enhanced by a new mutation operation and guided by the global bes...

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Bibliographic Details
Published in:Chinese Journal of Electronics 2015-10, Vol.24 (4), p.694-701
Main Authors: Tran, Dang Cong, Wu, Zhijian, Wang, Zelin, Deng, Changshou
Format: Article
Language:English
Subjects:
Artificial bee colony
bee
clustering
K-means
Artificial
colony
Muta
Data
Data clustering
EABCK
enhanced artificial bee colony algorithm
global best solution
hybrid data clustering algorithm
iterative methods
K‐means
K‐means clustering algorithm
Mutation operation
pattern clustering
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Summary:To improve the performance of K-means clustering algorithm, this paper presents a new hybrid approach of Enhanced artificial bee colony algorithm and Kmeans(EABCK). In EABCK, the original artificial bee colony algorithm(called ABC) is enhanced by a new mutation operation and guided by the global best solution(called EABC). Then, the best solution is updated by Kmeans in each iteration for data clustering. In the experiments, a set of benchmark functions was used to evaluate the performance of EABC with other comparative ABC variants. To evaluate the performance of EABCK on data clustering, eleven benchmark datasets were utilized. The experimental results show that EABC and EABCK outperform other comparative ABC variants and data clustering algorithms, respectively.
ISSN:1022-4653
2075-5597
DOI:10.1049/cje.2015.10.006