Robustness of density-based clustering methods with various neighborhood relations
Articles > Article · 2009
Authors: Efendi N. Nasibov, Gözde ULUTAGAY
Fuzzy Sets and Systems, Journal Indexed in SCI Expanded, International, Scopus, 2009, vol 160, pp 3601-3615
DOI: 10.1016/j.fss.2009.06.012
Summary
Cluster analysis is one of the most crucial techniques in statistical data analysis. Among the clustering methods, density-based methods have great importance due to their ability to recognize clusters with arbitrary shape. In this paper, robustness of the clustering methods is handled. These methods use distance-based neighborhood relations between points. In particular, DBSCAN (density-based spatial clustering of applications with noise) algorithm and FN-DBSCAN (fuzzy neighborhood DBSCAN) algorithm are analyzed. FN-DBSCAN algorithm uses fuzzy neighborhood relation whereas DBSCAN uses crisp neighborhood relation. The main characteristic of the FN-DBSCAN algorithm is that it combines the speed of the DBSCAN and robustness of the NRFJP (noise robust fuzzy joint points) algorithms. It is observed that the FN-DBSCAN algorithm is more robust than the DBSCAN algorithm to datasets with various shapes and densities. © 2009 Elsevier B.V. All rights reserved.