Abstract:

The fuzzy Cmeans clustering algorithm has a wide range of applications in data mining. Due to its sensitivity to the initial point and poor search ability, further applications of the algorithm are restricted. The artificial bee colony algorithm is not sensitive to the initial point and has remarkable searching ability and adaptability, however, it suffers slow convergence speed in solving onepeak problems, and it is easy to fall into local optimum faults in solving multipeak problems. Aiming at these problems, we introduce the mutation and crossover ideas of the differential evolution algorithm, which can improve the convergence speed of the swarm algorithm and balance its global and local search ability. We combine the improved artificial bee colony algorithm with the fuzzy Cmeans clustering algorithm, and run it on a number of international standard data sets, which verifies the proposed algorithm.Key words:

Key words: fuzzy C-means clustering;artificial bee colony algorithm;differential evolution algorithm;mutation;intersect