An enhanced Kernel Fuzzy C-Means Algorithm based on bio-inspired computing methods

In data analysis and data mining technique fields, one of the most widely used methods is clustering. Recently, one of the bio-inspired computing optimization algorithms called the Artificial Bee Colony (ABC) algorithm has been introduced, which has many characteristics, such as simple, robust, stochastic global optimization. In this paper, an enhancedKernel Fuzzy C-MeansAlgorithm (KFCM) based on theABC algorithm for data clustering is proposed. Compared with other popular bio-inspired computing optimization algorithms in data clustering, the results proved that the number of iterations is fewer, the convergence speed is faster and there is also a large improvement in the quality of clustering