Research on fuzzy clustering based on improved sparrow algorithm
Shaoming Qiu, Rui Li · 2022
Focusing the problem that the traditional fuzzy c-means clustering (FCM), which is a local search algorithm, it uses iterative hill climbing technology, which is sensitive to initial values and easy to fall into local minimums. An FCM method based on improved sparrow algorithm is proposed. Improve some problems that appear in Fuzzy c-means clustering (FCM) at this stage, and then combine them with association rules. First, through Logistic chaotic mapping, the initialization of the population can traverse the entire solution space and obtain the initial optimal position of the sparrow, so that it has a richer population and solves the problem of the randomness of the initialization population. Secondly, the adaptive t-distribution mutation operator is introduced into the sparrow algorithm, and the number of iterations of the algorithm is used as the degree of freedom parameter of the t-distribution to enhance the diversity of the population and greatly avoid the probability of the algorithm falling into local optimum. Then combine with the FCM algorithm to cluster the data. The innovation of this algorithm is that the sparrow algorithm and FCM are combined for the first time, combining the advantages of the two algorithms, the effect of data clustering is better, and the time of clustering is also accelerated. Compared with the proposed algorithm FCM algorithm, SSA-FCM algorithm and GA-FCM algorithm, it has stronger optimization ability and better clustering effect.