Abstract: Clustering plays an important role in data mining technology.Traditional clustering algorithms are hard clustering algorithms,namely,objects either belong to a class or do not belong to a class.However,when dealing with uncertain data,forced division will lead to decision-making errors.Three-way k-means clustering algorithm can divide the data into several groups with uncertain boundary reasonably.But it is still sensitive to the initial clustering center.In order to solve this problem,this paper presents a three-way k-means clustering algorithm based on artificial bee colony by integrating artificial bee colony algorithm with three-way k-means clustering algorithm.The fitness function of honey source is constructed by class cohesion function and inter class dispersion function to guide the bee colony to search for high-quality honey source globally.Using the cooperation and exchange of different roles between bee colonies,the data set is clustered repeatedly to find the optimal honey source location,which is used as the initial clustering center,and on this basis,iterative clustering is carried out alternately.Experiments show that this method improves the performance index of clustering results.The effectiveness of the algorithm is verified on UCI data set.

Key words: Three-way k-means, Artificial bee colony algorithm, Fitness function, Initial cluster center, Nectar