Clustering is a useful tool to explore data structures and have been employed in many disciplines. One of the most used techniques for clustering is based on K-means such that the data is partitioned into K clusters. Although K-means algorithm is easy to implement and works fast in most situations, it suffers from several drawbacks due to its choice of initializations and convergence to local optima. The K-harmonic means clustering solves the problem of initialization, but for the convergence to local optima, the K-harmonic means is hopeless. In this paper, a new method is proposed to solve the problem of convergence to local optima, namely particle swarm optimization K-harmonic means clustering (PSOKHM) algorithm. The experiment results on the three well known datasets show the effectiveness of the PSOKHM clustering algorithm.


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    Title :

    K-Harmonic Means Data Clustering with PSO Algorithm


    Additional title:

    Adv.Intel.,Soft Computing


    Contributors:
    Xie, Anne (editor) / Huang, Xiong (editor) / Nie, Fangyan (author) / Tu, Tianyi (author) / Pan, Meisen (author) / Rong, Qiusheng (author) / Zhou, Huican (author)


    Publication date :

    2012-01-01


    Size :

    7 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English





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