Performance of K-means algorithm based an ensemble learning

K-means is an iterative algorithm used with clustering task. It has more characteristics such as simplicity. In the same time, it suffers from some of drawbacks, sensitivity to initial centroid values that may produce bad results, they are based on centroids clusters would be selected randomly. More suggestions have been given in order overcome this problem. Ensemble learning a method clustering; multiple runs executed different results for data set. Then final driven. According hypothesis, ensemble techniques suggested deal One these "Three ways method". However, paper, three technique merged k-mean improve its performance and reduce impact results. was compared traditional k-means through practical work using popular The evaluation hypothesis done computing related metrics.