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
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;
Conference_Titel :
Pattern Recognition, 2000. Proceedings. 15th International Conference on
Conference_Location :
Barcelona
Print_ISBN :
0-7695-0750-6
DOI :
10.1109/ICPR.2000.906177
