基于Seed集的半监督核聚类

Semi-supervised kernel clustering algorithm based on seed set

提出了一种新的半监督核聚类算法——SKK-均值算法。算法利用一定数量的标记样本构成seed集,作为监督信息来初始化K-均值算法的聚类中心,引导聚类过程并约束数据划分;同时还采用了核方法把输入数据映射到高维特征空间,并用核函数来实现样本之间的距离计算。在UCI数据集上进行了数值实验,并与K-均值算法和核-K-均值算法进行了比较。

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.