PxEBCA: Proximity Expansion Based Clustering Algorithm

• Published in: Turkish journal of computer and mathematics education Vol. 12; no. 5; pp. 852 - 856
• Main Authors: Arora, Bhumika S, Chavda, Vijay, Pandya, Bhadresh R
• Format: Journal Article
• Language: English
• Published: Gurgaon Ninety Nine Publication 11.04.2021
• Subjects: Algorithms; Cluster analysis; Clustering; Computers; Data Analysis; Database Management Systems; Datasets; Density; Experiments; Multivariate Analysis; Noise; Object recognition; Outliers (statistics)
• ISSN: 1309-4653; 1309-4653
• DOI: 10.17762/turcomat.v12i5.1494

Cluster analysis is one of the main techniques for analysing data. It is a technique for detecting groups of objects which are similar without specifying any criteria for the grouping. The matter of detecting clusters is challenging when the clusters are of varied size, density and shape. DBSCAN can find arbitrary shaped clusters along with outliers but it cannot handle different density. This paper presents a new method for detecting density based clusters which works on datasets having varied density. In this paper we propose PxEBCA that discovers clusters with arbitrary shape and also with varying density.Experimental evaluation of the effectiveness and efficiency of PEBCA was done using synthetic data. The results of experiments demonstrated that PxEBCA is significantly more effective in discovering clusters of arbitrary shapes with varying densities.