Robustness of density-based clustering methods with various neighborhood relations

Authors:
Efendi N. Nasibov;Gözde Ulutagay
Affiliations:
Department of Statistics, Faculty of Science and Arts, Dokuz Eylul University, Tinaztepe Campus, 35160 Buca, Izmir, Turkey;Department of Computer Engineering, Izmir University, Gursel Aksel Blv. No.14, 35350 Uckuyular, Izmir, Turkey
Venue:
Fuzzy Sets and Systems
Year:
2009

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Abstract

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.