Clustering Algorithm Based on Improved Particle Swarm Algorithm

Abstract:

K-means algorithm is a traditional cluster analysis method, has the characteristics of simple ideas and algorithms, and thus become one of the commonly used methods of cluster analysis. However, the K-means algorithm classification results are too dependent on the choice of the initial cluster centers for some initial value, the algorithm may converge in general suboptimal solutions. Analysis of the K-means algorithm and particle swarm optimization based on a clustering algorithm based on improved particle swarm algorithm. The algorithm local search ability of the K-means algorithm and the global search ability of particle swarm optimization, local search ability to improve the K-means algorithm to accelerate the convergence speed effectively prevent the occurrence of the phenomenon of precocious puberty. The experiments show that the clustering algorithm has better convergence effect.

• Periodical: Advanced Materials Research (Volumes 798-799)
• Pages: 689-692
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.798-799.689
• Online since: September 2013
• Authors: Jin Hui Yang, Xi Cao
• Keywords: Cluster Analysis, Convergence Rate, Globally Optimal, K-Means Algorithm, Particle Swarm Algorithm

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