Improved artificial bee colony clustering algorithm based on fuzzy C-means

Due to the issues of traditional fuzzy C-means clustering algorithm, which is sensitive to the initial selection of the center and noise data, and the disadvantages of weak local search ability and development capability in standard artificial bee colony algorithm, this paper modifies artificial bee colony algorithm inspired by the thought of differential evolution and makes a more accurate description of searching behavior of onlooker bees. Fuzzy C-means clustering algorithm has advantages of fast converges, the ability of local search and is easy to implement. Combination of them can improve the performance of clustering. The experiment shows that compared with traditional fuzzy C-means algorithm, the algorithm further improves the accuracy and noise immunity and has better clustering results.