Semi-supervised kernel clustering algorithm based on seed set

doi:10.3778/j.issn.1002-8331.2009.20.046

Computer Engineering and Applications ›› 2009, Vol. 45 ›› Issue (20): 154-157.DOI: 10.3778/j.issn.1002-8331.2009.20.046

• 数据库、信息处理 • Previous Articles Next Articles

Semi-supervised kernel clustering algorithm based on seed set

LI Kun-lun，ZHANG Chao，CAO Zheng，LIU Ming

College of Electronic and Information Engineering，Hebei University，Baoding，Hebei 071002，China

Received:2009-01-09 Revised:2009-04-01 Online:2009-07-11 Published:2009-07-11
Contact: LI Kun-lun

基于Seed集的半监督核聚类

李昆仑，张超，曹铮，刘明

河北大学电子信息工程学院，河北保定 071002

通讯作者: 李昆仑

Abstract

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

摘要： 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。

关键词: 半监督聚类, seed 集, 核方法, K-均值

LI Kun-lun，ZHANG Chao，CAO Zheng，LIU Ming. Semi-supervised kernel clustering algorithm based on seed set[J]. Computer Engineering and Applications, 2009, 45(20): 154-157.

李昆仑，张超，曹铮，刘明. 基于Seed集的半监督核聚类[J]. 计算机工程与应用, 2009, 45(20): 154-157.

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Semi-supervised kernel clustering algorithm based on seed set

基于Seed集的半监督核聚类

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