一种基于竞争型群体优化的数据聚类方法

数据聚类在智能信息处理中具有非常重要的作用。传统的数据聚类方法,如K-means算法,存在对初始聚类中心敏感等问题。随着智能优化算法的发展,人们用智能优化算法进行数据聚类取得了一定的效果,但存在容易陷入局部最优等问题。为此,本文将在高维优化问题中取得良好效果的竞争型群体优化算法中引入数据聚类,利用竞争型群体优化算法强大的全局探索能力搜索聚类中心进行数据聚类,在UCI的5个数据集上的实验结果表明竞争型群体优化算法比遗传算法、粒子群算法不仅能得到更好的聚类效果,而且收敛性能更好。

A Data Clustering Method Based on Competitive Swarm Optimizer

Data clustering plays a very important role in intelligent information processing, but the traditional K-means algorithm is sensitive to initial clustering centers. With the development of intelligent optimization algorithm, people uses intelligent optimization algorithm to cluster data and achieve a certain effect, but it is still easy to fall into the local optimization. In this paper, the competitive swarm optimizer algorithm which has achieved good results in high dimensional optimization problem is exploited for data clustering, the powerful exploration ability of competitive swarm optimizer is used to search clustering centers for data clustering. The experimental results on the five data sets of UCI show that the competitive swarm optimizer can not only get better clustering effect but also better convergence performance than genetic algorithm and particle swarm optimization algorithm.