An improved IWO-FCM Data Mining Algorithm

The FCM algorithm based on invasive weed optimization algorithm (IWO-FCW) has stronger global optimization ability and higher clustering precision than the basic FCM algorithm, but the IWO-FCW algorithm exists some questions that the convergence become slow and the clustering precision is not high for high and complex testing data sets. So an improved IWO-FCM algorithm is proposed in this paper. This algorithm uses the chaos sequence to initialize the initial population in order to improve initial solution (seed) quality, then the crossover, mutation and part selection operation of the differential evolution algorithm are applied in the spatial distribution and selection process of IWO-FCM algorithm to keep the population diversity and enhance global optimization ability. By testing multiple high-dimensional data sets, the simulation results show that the proposed algorithm has faster convergence speed and higher optimization precision than FCM algorithm and IWO-FCM algorithm.