Advanced differential evolution algorithm for clustering on Weka
Zuo Feng-chao · Jisuanji gongcheng yu sheji · 2012
After analyzing the drawbacks of the K-means algorithm,a novel differential evolution algorithm for solving clustering problem is proposed to optimize the criterion function for clustering.In order to further enhance the capability of global search,a self-adaptive strategy using fitness variance of the population is introduced to adjust scaling factor and crossover probability.The proposed approach is implemented on the Weka platform where its classes and interfaces are fully utilized.At last,the proposed algorithm is tested on three UCI datasets and compared with K-means.The simulation results indicate that the proposed algorithm can acquire better clustering performance.