@article{MAKHILLIJSC20072520880,
    title = {A Co-Evolutionary K-means Algorithm},
    journal = {International Journal of Soft Computing},
    volume = {2},
    number = {5},
    pages = {624-627},
    year = {2007},
    issn = {1816-9503},
    doi = {ijscomp.2007.624.627},
    url = {https://makhillpublications.co/view-article.php?issn=1816-9503&doi=ijscomp.2007.624.627},
    author = {Sung Hae Jun and},
    keywords = {K-means clustering,the number of clusters,co-evolutionary computing},
    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.}
    }