Clustering by kernel density

Authors:
Christian Mauceri;Diem Ho
Affiliations:
IBM Europe, Courbevoie, France 92066 and École Nationale Supérieure des Télécommunications --- Bretagne, Technopôle Brest-Iroise, Brest, France 29238;IBM Europe, Courbevoie, France 92066
Venue:
Computational Economics
Year:
2007

Citing 4
Cited 0

Data clustering: a review

ACM Computing Surveys (CSUR)
A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery
Support vector clustering

The Journal of Machine Learning Research
Minimum Entropy Clustering and Applications to Gene Expression Analysis

CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference

Quantified Score

Hi-index	0.00

Visualization

Abstract

Kernel methods have been used for various supervised learning tasks. In this paper, we present a new clustering method based on kernel density. The method does not make any assumption on the number of clusters or on their shapes. The method is simple, robust, and behaves equally or better than other methods on problems known as difficult.