A novel kernel method for clustering

Kernel methods are algorithms that, by replacing the inner product with an appropriate positive definite function, implicitly perform a nonlinear mapping of the input data into a high-dimensional feature space. In this paper, we present a kernel method for clustering inspired by the classical k-mean...

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Bibliographic Details
Published inIEEE transactions on pattern analysis and machine intelligence Vol. 27; no. 5; pp. 801 - 805
Main Authors Camastra, F., Verri, A.
Format Journal Article
LanguageEnglish
Published Los Alamitos, CA IEEE 01.05.2005
IEEE Computer Society
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Applied sciences
Artificial Intelligence
Benchmarks
Breast cancer
Breast Neoplasms - diagnosis
Cluster Analysis
Clustering
Clustering algorithms
Computer science; control theory; systems
Computer Simulation
Connectionism. Neural networks
Diagnosis, Computer-Assisted - methods
EM algorithm
Exact sciences and technology
Humans
Index Terms- Kernel methods
Information Storage and Retrieval - methods
Intelligence
Iris
Iterative algorithms
K-Means
Kernel
Kernels
Models, Biological
Models, Statistical
one class SVM
Pattern analysis
Pattern Recognition, Automated - methods
Quantization
Reproducibility of Results
Self organizing feature maps
Sensitivity and Specificity
Spatial databases
Support vector machine classification
Support vector machines
Kernel methods
K-Means
clustering algorithms
Inner product
one class SVM
K means algorithm
Support vector machine
Clustering
EM algorithm
Kernel method
Online AccessGet full text
ISSN0162-8828
1939-3539
DOI10.1109/TPAMI.2005.88

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Summary:Kernel methods are algorithms that, by replacing the inner product with an appropriate positive definite function, implicitly perform a nonlinear mapping of the input data into a high-dimensional feature space. In this paper, we present a kernel method for clustering inspired by the classical k-means algorithm in which each cluster is iteratively refined using a one-class support vector machine. Our method, which can be easily implemented, compares favorably with respect to popular clustering algorithms, like k-means, neural gas, and self-organizing maps, on a synthetic data set and three UCI real data benchmarks (IRIS data, Wisconsin breast cancer database, Spam database).
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ISSN:0162-8828
1939-3539
DOI:10.1109/TPAMI.2005.88