Open AccessA Novel Hybrid Data Clustering Algorithm Based on Artificial Bee Colony Algorithm and K ‐Means
Author(s) -
Tran Dang Cong,
Wu Zhijian,
Wang Zelin,
Deng Changshou
Publication year - 2015
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2015.10.006
Subject(s) - cluster analysis , benchmark (surveying) , computer science , artificial bee colony algorithm , algorithm , set (abstract data type) , data set , k means clustering , canopy clustering algorithm , data mining , artificial intelligence , correlation clustering , geodesy , programming language , geography
To improve the performance of K ‐means clustering algorithm, this paper presents a new hybrid approach 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 mutation 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 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.