Robust and efficient cluster analysis using a shared near neighbours approach

Author

Hofman, Irving ; Jarvis, Ray

Author_Institution

Dept. of Electr. & Comput. Syst. Eng., Monash Univ., Clayton, Vic., Australia

Volume

1

fYear

1998

fDate

16-20 Aug 1998

Firstpage

243

Abstract

A nonparametric method for clustering multidimensional data in O(nlogn) time is described. It is based on the shared near neighbours algorithm. It uses adaptive k-d trees combined with various other sophisticated data structures to significantly decrease the computational complexity of the original algorithm which was O(n²). The algorithm is suitable for a wide range of data and capable of delineating clusters of varying shape, density, and homogeneity. A comprehensive set of results is presented

Keywords

computational complexity; pattern recognition; tree data structures; adaptive k-d trees; computational complexity; multidimensional data clustering; nonparametric method; robust efficient cluster analysis; shared near neighbours approach; sophisticated data structures; Clustering algorithms; Clustering methods; Computational complexity; Data structures; Electrical capacitance tomography; Intelligent robots; Partitioning algorithms; Robustness; Shape; Systems engineering and theory;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 1998. Proceedings. Fourteenth International Conference on

Conference_Location

Brisbane, Qld.

ISSN

1051-4651

Print_ISBN

0-8186-8512-3

Type

conf

DOI

10.1109/ICPR.1998.711126

Filename

711126