Rough K-means Clustering Algorithm Combined with Artificial Bee Colony Optimization

Abstract

The rough K-means clustering algorithm has strong ability to deal with data with uncertain boundaries. However, this algorithm also has limitations such as sensitivity to the selection of initial clustering centers, and use of fixed weights and thresholds resulting in unstable clustering results and decreased accuracy. A lot of research has been devoted to solving these problems from different angles. With introduction of artificial bee colony (ABC) algorithm, the algorithm is improved from three aspects. Firstly, based on the ratio of the number of objects in lower approximate set and the boundary set to the product of the difference of the objects in the dataset, a more reasonable method of dynamically adjusting the weights of approximation and boundary set is designed. Secondly, in order to speed up the convergence speed of the algorithm, an implementation method of adaptive threshold ε associated with the number of iterations is given. Thirdly, by constructing the fitness function of the nectar source location, the bee colony is guided to search for high-quality nectar sources globally. The best position of honey source obtained by each iteration is taken as the initial cluster center, and the cluster is carried out on the basis of this. Experimental results show that the improved algorithm improves the stability of the clustering results and obtains better clustering effect.

Keywords

• |rough k-means algorithm|artificial bee colony (abc) algorithm|nectar|cluster center|fitness function