Semi-Supervised Kernel Clustering Algorithm based on Seed Set

被引：2

作者：

Li, Kunlun ^{[1
]}

Zhang, Chao ^{[1
]}

Cao, Zheng ^{[1
]}

机构：

[1] Hebei Univ, Coll Elect & Informat Engn, Baoding, Peoples R China

来源：

2009 ASIA-PACIFIC CONFERENCE ON INFORMATION PROCESSING (APCIP 2009), VOL 1, PROCEEDINGS | 2009年

关键词：

seed; semi-supervised clustering; kernel K-means;

D O I：

10.1109/APCIP.2009.50

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：169 / 172

页数：4

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