TY  - JOUR
T1  - A Co-Evolutionary K-means Algorithm
AU - , Sung Hae Jun AU - , Im Geol Oh 
JO  - International Journal of Soft Computing
VL  - 2
IS  - 5
SP  - 624
EP  - 627
PY  - 2007
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2007.624.627
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2007.624.627
KW  - K-means clustering
KW  -the number of clusters
KW  -co-evolutionary computing
AB  - 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.
ER  -