关键词: 半监督聚类, seed 集, 核方法, K-均值

Abstract: This paper presents a novel semi-supervised kernel clustering algorithm called Seed Kernel K-Means（SKK-Means） algorithm.It uses labeled data to generate initial seed clusters to guide the clustering process and data partition，and uses kernel method to map the input data into a high-dimensional feature space and calculates the distance between data points with a kernel function.The algorithm is compared with the other clustering algorithms such as K-Means and Kernel K-Means，on UCI databases in some numeric experiment.

Key words: semi-supervised clustering, seed set, kernel method, K-means