Robustness of density-based clustering methods with various neighborhood relations

Nasibov E., Ulutagay G.

FUZZY SETS AND SYSTEMS, vol.160, no.24, pp.3601-3615, 2009 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 160 Issue: 24
  • Publication Date: 2009
  • Doi Number: 10.1016/j.fss.2009.06.012
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.3601-3615
  • Keywords: Clustering, Fuzzy neighborhood, FJP, DBSCAN, FN-DBSCAN, ALGORITHM, VALIDITY
  • Dokuz Eylül University Affiliated: Yes


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. (C) 2009 Elsevier B.V. All fights reserved.