Determination of the appropriate parameters for K-means clustering using selection of region clusters based on density DBSCAN (SRCD-DBSCAN)

K-means clustering can be highly accurate when the number of clusters and the initial cluster centre are appropriate. An inappropriate determination of the number of clusters or the initial cluster centre decreases the accuracy of K-means clustering. However, determining these values is problematic. To solve these problems, we used density-based spatial clustering of application with noise (DBSCAN) because it does not require a predetermined number of clusters; however, it has some significant drawbacks. Using DBSCAN with high-dimensional data and data with potentially different densities decreases the accuracy to some degree. Therefore, the objective of this research is to improve the efficiency of DBSCAN through a selection of region clusters based on density DBSCAN to automatically find the appropriate number of clusters and initial cluster centres for K-means clustering. In the proposed method, DBSCAN is used to perform clustering and to select the appropriate clusters by considering the density of each cluster. Subsequently, the appropriate region data are chosen from the selected clusters. The experimental results yield the appropriate number of clusters and the appropriate initial cluster centres for K-means clustering. In addition, the results of the selection of region clusters based on density DBSCAN method are more accurate than those obtained by traditional methods, including DBSCAN and K-means and related methods such as Partitioning-based DBSCAN (PDBSCAN) and PDBSCAN by applying the Ant Clustering Algorithm DBSCAN (PACA-DBSCAN).