Semi-supervised Kernel Clustering Algorithm Based on Seed Set

Explore a semi-supervised clustering algorithm called seed kernel K-means (SKK-means) which is inspired by the kernel method and seeding strategy based on the classical K-means algorithm. The algorithm uses a certain ratio of data points as the seeds to generate initial cluster centers, and maps the data into feature space using kernel method. Our algorithm, which can be easily implemented, compares with respect to the other algorithm such as K-means and Kernel K-means, on 3 UCI databases (IRIS, Crabs and New-Thyroid) in some numeric experiment.