A novel kernel method for clustering

F. Camastra, A. Verri
2005 IEEE Transactions on Pattern Analysis and Machine Intelligence  
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
more » ... opular 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).
doi:10.1109/tpami.2005.88 pmid:15875800 fatcat:54tudmpgbvhlxh6kuqgmi3kn3q