A Robust Clustering Algorithm Based on Competitive Agglomeration and Soft Rejection of Outliers

Authors:
Hichem Frigui;Raghu Krishnapuram
Affiliations:
-;-
Venue:
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Year:
1996

Citing 0
Cited 6

A Robust Competitive Clustering Algorithm With Applications in Computer Vision

IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Analysis through a Markovian Modelling and FuzzyClassification: Application to Urban Area Extraction fromSatellite Images

International Journal of Computer Vision
Nonlinear system identification based on adaptive competitive clustering and OLS

CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
Towards early warning systems: challenges, technologies and architecture

CRITIS'09 Proceedings of the 4th international conference on Critical information infrastructures security
Personalized web recommendation based on path clustering

ICCOMP'06 Proceedings of the 10th WSEAS international conference on Computers
Personalized web recommendation based on path clustering

FQAS'06 Proceedings of the 7th international conference on Flexible Query Answering Systems

Quantified Score

Hi-index	0.00

Visualization

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.