Kernel Methods are algorithms that implicitly perform, by replacing the inner product with an appropriate Mercer Kernel, a nonlinear mapping of the input data to a high dimensional Feature Space. In this paper, we describe a Kernel Method for clustering. The algorithm compares better with popular clustering algorithms, namely K-Means, Neural Gas, Self Organizing Maps, on a synthetic dataset and three UCI real data benchmarks, IRIS data, Wisconsin breast cancer database, Spam database.