A Complexity Survey on Density based Spatial Clustering of Applications of Noise Clustering Algorithms

Abstract

Data Clustering is an interesting field of unsupervised learning that has been extensively used and discussed over several research papers and scientific studies. It handles several issues related to data analysis by grouping similar entities into the same set. Up to now, many algorithms were developed for clustering using several techniques including centroids, density and dendrograms approaches. We count nowadays more than 100 diverse algorithms and many enhancements for each algorithm. Therefore, data scientists still struggle to find the best clustering method to use among this diversity of techniques. In this paper we present a survey on DBSCAN algorithm and its enhancements with respect to time requirement. A significant comparison of DBSCAN versions is also illustrated in this paper to help data scientist make decisions about the best version of DBSCAN to use.