Abstract:
The classical approach of using minimum cut criterion for clustering is often ineffective due to the existence of outliers in the data. This paper presents a novel normal...Show MoreMetadata
Abstract:
The classical approach of using minimum cut criterion for clustering is often ineffective due to the existence of outliers in the data. This paper presents a novel normalized graph sampling algorithm for clustering that improves the solution of clustering via the incorporation of a priori constraint in a stochastic graph sampling procedure. The quality of the proposed algorithm is empirically evaluated on two synthetic datasets and a color medical image database.
Published in: 2002 International Conference on Pattern Recognition
Date of Conference: 11-15 August 2002
Date Added to IEEE Xplore: 10 December 2002
Print ISBN:0-7695-1695-X
Print ISSN: 1051-4651