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Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition (1996)
San Francisco, Ca.
June 18, 1996 to June 20, 1996
ISSN: 1063-6919
ISBN: 0-8186-7258-7
pp: 550
Hichem Frigui , Univeristy of Missouri Frigui or raghu@sun1.ece.missouri.edu
Raghu Krishnapuram , Univeristy of Missouri Frigui or raghu@sun1.ece.missouri.edu
ABSTRACT
We present a new clustering algorithm that addresses two major issues associated with conventional partitional clustering: the difficulty in determining the number of clusters, and the sensitivity to noise and outliers. The proposed algorithm determines the number of clusters by a process of competitive agglomeration. Noise immunity is achieved by integrating concepts from robust statistics into the algorithm. The proposed approach can incorporate different distance measures in the objective function to find an unknown number of clusters of various types including lines, planes and surfaces.
INDEX TERMS
Robust clustering, outlier detection, robust parameter estimation, heterogeneous estimation, competitive agglomeration
CITATION

H. Frigui and R. Krishnapuram, "A Robust Clustering Algorithm Based on Competitive Agglomeration and Soft Rejection of Outliers," Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR), San Francisco, Ca., 1996, pp. 550.
doi:10.1109/CVPR.1996.517126
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