K-Means Performance Optimization Using Rank Order Centroid (ROC) And Braycurtis Distance

Abstract

K-Means is a clustering algorithm that groups data based on similarities between data. Some of the problems that arise from this algorithm are when determining the center point of the cluster randomly. This will certainly affect the final result of a clustering process. To anticipate the poor accuracy value, a process is needed to determine the initial centroid in the initialization process. The second problem is when calculating the Euclidean distance on the distance between data. However, this method only gives the same impact on each data attribute. From some of these problems, this study proposes the Rank Order Centroid (ROC) method for initializing the cluster center point and using the Braycurtis distance method to calculate the distance between data. With the experiment K=2 to K=10, the results obtained in this study are the proposed method obtains an iteration reduction of 6.6% on the Student Performance Exams dataset and 19.3% on the Body Fat Prediction dataset. However, there was an increase in iterations on the Heart Failure dataset by 24.2%. In testing the cluster results using the Silhouette Coefficient, this method shows an increase in the evaluation value of 5.9% in the Student Performance Exams dataset. However, the evaluation value decreased by 8.3% in the Body Fat Prediction dataset and 3.3% in the Heart Failure dataset.