Clustering Algorithm Based on Improved Particle Swarm Optimization

K-mean algorithm,a traditional clustering method with simple characteristic of thought and algorithm,has therefore become one of the methods commonly used in cluster analysis. But the K-means algorithm classification results depend on the initial cluster centers choice.For some initial value,the algorithm may converge to the general sub-optimal solution. This paper proposes a clustering algorithm based on the influence of neighborhood improvement particle swarm optimization( PSO). Through the improved PSO algorithm,we can optimize the combination of K-means clustering algorithm. Both the K-mean,which has strong capacity of local searching,and the PSO,which has power global search ability,are combined. It not only improves the K-mean' s local searching capacity,accelerates the convergence rate,but also effectively prevents the premature convergence. The experiments show that this clustering algorithm has better convergence. On the one hand the use of clustering is shorter; on the other hand the accuracy is higher.