DocumentCode
1743027
Title
A support vector clustering method
Author
Ben-Hur, Asa ; Horn, David ; Siegelmann, Hava T. ; Vapnik, Vladimir
Author_Institution
Fac. of Ind. Eng. & Manage., Technion-Israel Inst. of Technol., Haifa, Israel
Volume
2
fYear
2000
fDate
2000
Firstpage
724
Abstract
We present a novel kernel method for data clustering using a description of the data by support vectors. The kernel reflects a projection of the data points from data space to a high dimensional feature space. Cluster boundaries are defined as spheres in feature space, which represent complex geometric shapes in data space. We utilize this geometric representation of the data to construct a simple clustering algorithm
Keywords
learning automata; pattern clustering; cluster boundaries; complex geometric shapes; data clustering; data point projection; data space; geometric representation; high-dimensional feature space; support vector clustering method; support vector machines; Clustering algorithms; Clustering methods; Engineering management; Industrial engineering; Kernel; Lagrangian functions; Physics; Shape; Support vector machine classification; Support vector machines;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition, 2000. Proceedings. 15th International Conference on
Conference_Location
Barcelona
ISSN
1051-4651
Print_ISBN
0-7695-0750-6
Type
conf
DOI
10.1109/ICPR.2000.906177
Filename
906177
Link To Document