首页|A Novel Hybrid Data Clustering Algorithm Based on Artificial Bee Colony Algorithm and K-Means
A Novel Hybrid Data Clustering Algorithm Based on Artificial Bee Colony Algorithm and K-Means
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NETL
NSTL
万方数据
To improve the performance of K-means clustering algorithm, this paper presents a new hybrid ap-proach of Enhanced artificial bee colony algorithm and K-means (EABCK). In EABCK, the original artificial bee colony algorithm (called ABC) is enhanced by a new mu-tation operation and guided by the global best solution (called EABC). Then, the best solution is updated by K-means in each iteration for data clustering. In the experi-ments, 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 out-perform other comparative ABC variants and data clus-tering algorithms, respectively.
Data clusteringK-meansArtificial bee colonyMutation operation
NETL
NSTL
万方数据
