On parameter adjustment of the fuzzy neighborhood-based clustering algorithms

Abstract

Day by day huge amounts data are produced, and evaluation of these data becomes more difficult. Thedata obtained should provide meaningful, correct, and accurate information. Therefore, all data must be separatedinto clusters correctly, and the right information from these clusters must be obtained. Having the correct clustersdepends on the clustering algorithm that is used. There are many clustering algorithms. The density-based methods arevery important among the groups of clustering methods, as they can find arbitrary shapes. An advanced model of thedensity-based spatial clustering of applications with noise (DBSCAN) algorithm, called fuzzy neighborhood DBSCANGaussian means (FN-DBSCAN-GM), is offered in this study. The main contribution of FN-DBSCAN-GM is to findthe parameters automatically and to divide the data into clusters robustly. The effectiveness of FN-DBSCAN-GM hasbeen demonstrated on overlapping datasets (six artificial and two real-life datasets). The performances of these datasetsare compared with the percentage of correct classification and validity index. Our experiments showed that this newalgorithm was a preferable and robust algorithm.