Article ID Journal Published Year Pages File Type
390941 Fuzzy Sets and Systems 2009 15 Pages PDF
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.

Related Topics
Physical Sciences and Engineering Computer Science Artificial Intelligence