Sung Hae Jun  , Im Geol Oh  , 
    A Co-Evolutionary K-means Algorithm,
    International Journal of Soft Computing,
    Volume 2,Issue 5,
    2007,
    Pages 624-627,
    ISSN 1816-9503,
    ijscomp.2007.624.627,
    (https://makhillpublications.co/view-article.php?doi=ijscomp.2007.624.627)
    Abstract: Clustering is an important tool for data mining. Its aim is to assign the points into groups that are homogeneous within a group and heterogeneous between groups. Many works of clustering methods have been researched in diverse machine learning fields. An efficient algorithm of clustering is K-means algorithm. This is a partitioning method. Also K-means algorithm has offered good clustering results. As well other clustering methods, K-means algorithm has some problems. One of them is optimal selection of the number of clusters. In K-means algorithm, the number of cluster K is determined by the art of researchers. In this study, we propose a co-evolutionary K-means(CoE K-means) algorithm for overcoming the problem of K-means algorithm. Our CoE K-means algorithm combines co-evolutionary computing into K-means algorithm. In our experimental results, we verify improved performances of CoE K-means algorithm using simulation data.
    Keywords: K-means clustering;the number of clusters;co-evolutionary computing