A support vector clustering method

Asa Ben‐Hur; D. Horn; Hava T. Siegelmann; Vladimir Vapnik

doi:10.1109/icpr.2000.906177

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

Cluster analysisComputer scienceKernel (algebra)Feature vectorPattern recognition (psychology)Kernel methodClustering high-dimensional dataArtificial intelligenceProjection (relational algebra)Correlation clusteringCURE data clustering algorithmData spaceData miningMathematicsSupport vector machineAlgorithm

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Publication Info

Year: 2002
Type: article
Volume: 2
Pages: 724-727
Citations: 159
Access: Closed

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APA Style

                            
                                    Asa Ben‐Hur, 
                                
                                    D. Horn, 
                                
                                    Hava T. Siegelmann
                                
                                et al.
                            
                            (2002). 
                            A support vector clustering method. 
                            
                            , 2
                            
                            , 724-727.
                            https://doi.org/10.1109/icpr.2000.906177

Identifiers

DOI: 10.1109/icpr.2000.906177