An improved fuzzy C-means clustering algorithm based on simulated annealing

Fuzzy C-means clustering algorithm (FCM) is a widely used clustering algorithm, however it has its drawbacks: the initial number of clusters needs to be determined by the manual control according to the prior knowledge; the objective function ignores the disequilibrium problems among the sample attribute data. In view of these problems, this paper proposes a sample weighted FCM algorithm based on simulated annealing algorithm. It uses the simulated annealing algorithm which has an excellent ability of seeking global optimal solution to calculate the initial value of the number of clusters and makes certain weighting process on the clustering center function and the objective function. The experiment results show that this proposed algorithm has better classification accuracy and classification accuracy rate compared with FCM algorithm and the common sample weighted FCM clustering algorithms. Meanwhile, this algorithm needs not to be determined the initial value of clusters manually. The improved algorithm possesses the superiority and the actual application value.