Data clustering is a popular data analysis technique needed in many fields. Recent years, some swarm intelligence-based approaches for clustering were proposed and achieved encouraging results. This paper presents a Hybrid Artificial Bee Colony (HABC) algorithm for data clustering. The incentive mechanism of HABC is enhancing the information exchange (social learning) between bees by introducing the crossover operator of Genetic Algorithm (GA) to ABC. With a test on ten benchmark functions, the proposed HABC algorithm is proved to have significant improvement over canonical ABC and several other comparison algorithms. The HABC algorithm is then employed for data clustering. Six real datasets selected from the UCI machine learning repository are used. The results show that the HABC algorithm achieved better results than other algorithms and is a competitive approach for data clustering.
Xiaohui Yan et al. REF have presented a Hybrid Artificial Bee Colony (HABC) algorithm, in which the crossover operator of GA was introduced in to improve the original ABC algorithm.
A new approach for data clustering using hybrid artificial bee colony algorithm
Neurocomputing
Neurocomputing
Computer Science Mathematics