Performance Analysis of Subtractive Clustering Algorithm in Determining the Number and Position of Cluster Centers - Irwandi Irwandi; Opim Salim Sitompul; Rahmat Widia Sembiring (RIRAI Publisher, 2021)
The basic concept of the subtractive clustering algorithm is to choose a data point that has the highest density (potential) in a space (variable) as the center of the cluster. The number and position of the cluster centers formed are influenced by the given radius (r) parameter value. If the radius value is very small, it will result in the neglect of potential data points around the center of the cluster. If the value of the radius parameter is too large, it increases the contribution of all potential data points, thereby canceling the effect of cluster density. The number of cluster centers in the subtractive clustering algorithm is determined based on the iteration process in finding data points with the highest number of neighbors. This study uses the clustering partition as a parameter value to determine a data point (candidate cluster center) will be selected to determine the effect of the radius (r) parameter value on the subtractive clustering algorithm in generating clustering. From the experiments that have been carried out on 4 datasets, the results have been obtained, for dataset 1 the highest average value of fuzzy silhouette with a parameter value of radius (r) 0.35 is 0.9088 and the number of clusters 2. While in dataset 2, the average value The highest fuzzy silhouette with a parameter value of radius (r) 0.40 is 0.6742 and the number of clusters 3. While in dataset 3, the average value of the highest fuzzy silhouette with a parameter value of radius (r) 0.50 is 0.7434 and the number of clusters 3. While in the dataset the last is the fourth dataset, the highest fuzzy silhouette average value with a radius (r) parameter value of 0.50 is 0.6630 and the number of clusters 2. This subscractive clustering algorithm is widely applied in the fields of transportation, GIS, big data, control of electric voltages, electrical energy needs, knowing the area of population density to health such as breast cancer diagnosis, which is related to the needs of human life.