Copyright (c) 2023 Mustikasari Mustikasari, Nur Salman
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DENSITY-BASED CLUSTERING ANALYSIS WITH DBSCAN AND OPTICS
Corresponding Author(s) : Mustikasari Mustikasari
Jurnal INSYPRO (Information System and Processing),
Vol. 8 No. 1 (2023)
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.