Performance of K-means algorithm based an ensemble learning

Abstract

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

Performance analysis of demand forecasting in energy consumption based on ensemble model

“…In this classification, the target variable and class variables are divided based on the fields. By analysing the classification accuracy, the ensemble model is proved to shows the best accuracy in energy model [41], [42]. The main task after classification is to identify how far, the classification algorithm works on different set of same data.…”