UIFDBC: Effective density based clustering to find clusters of arbitrary shapes without user input. (30th December 2021)

• Record Type: Journal Article
• Authors: Chowdhury, Hussain Ahmed; Bhattacharyya, Dhruba Kumar; Kalita, Jugal Kumar
• Is Part Of: Expert systems with applications. Volume 186(2021)
• Publication Date: 2021-12-30
• Subjects: Density-based clustering -- Cluster validity index -- Optimal clusters -- Arbitrary shapes clusters; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2021.115746
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: Density-based clustering has the ability to detect arbitrary shaped clusters in any dataset. In recent years, several density peak clustering methods have been reported. Among these, a few need user input(s), but majority use cluster validity indices to provide the best results. In this paper, we propose a density-based user-input-free clustering method named UIFDBC, which is capable of detecting clusters of arbitrary shapes, without depending on any specific cluster validity index. The method is evaluated on 16 synthetic and 7 real-world datasets and compared with 8 recent density-based clustering methods. The results show our method is superior, in general, to its counterparts in terms of discovering arbitrary shaped clusters on tested datasets. The approach also has the ability to handle low-density instances in a special manner to minimize error propagation. Our method is available as an R package and can be downloaded by clicking the link https://sites.google.com/view/hussinchowdhury/software .