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-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).
Camastra
Francesco
F
INFM-DISI, Università di Genova, Via Dodecaneso 35, 16146 Genova, Italy. camastra@ieee.org
Verri
Alessandro
A
D000465:Algorithms; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000175:diagnosis*; D016000:Cluster Analysis; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity