Efficiency comparisons between k-centers and k-means algorithms

Abstract

© 2015 IEEE. This paper proposes an under-sampling method with an algorithm which guarantees the sampling quality called k-centers algorithm. Then, the efficiency of the sampling using under-sampling method with k-means algorithm is compared with the proposed method. For the comparison purpose, four datasets obtained from UCI database were selected and the RIPPER classifier was used. From the experimental results, our under-sampling method with k-centers algorithm provided the Accuracy, Recall, and F-measure values higher than that obtained from the under-sampling with k-means algorithm in every dataset we used. The Precision value from our k-centers algorithm might be lower in some datasets, however, its average value computed out of all datasets is still higher than using the under-sampling method with k-means algorithm. Moreover, the experimental results showed that our under-sampling method with k-centers algorithm also decreases the Accuracy value obtained from the original data less than that using the under-sampling with k-means algorithm.