DENSITY-BASED CLUSTERING ANALYSIS WITH DBSCAN AND OPTICS ANALISIS KLASTER BERBASIS KEPADATAN DENGAN DBSCAN DAN OPTICS

Abstract

This paper describes the process of cluster analysis on the DBSCAN density-based clustering algorithm and the OPTICS augmentation algorithm implemented in R.. Compared to other implementations, DBSCAN offers an implementation that can leverage advanced data such as k-d trees to speed up calculations. An important advantage of this implementation is the ability of both algorithms to handle data, especially granular data with various forms, which conventional distance-based separation algorithms often cannot handle because of the difficulty of identifying the center of a data cluster. A simple comparison is shown to give insight into the advantages of this density-based method. Experiments with the implementation of DBSCAN and OPTICS compared to other popular algorithms show that DBSCAN implemented in R provides a fast, strong, and efficient solution.