Initial Cluster Centers Based on Moving Two Lines Approximation in K-means Algorithm

Author:

Feng Wenyue,Xuan Shuangxia

Abstract

The main shortcoming of K-means clustering algorithm is its great dependence on the initial cluster center point. Based on the moving two lines approximation model, this paper gives a method to pick the initial cluster center of k-means clustering. Numerical experiments and comparison criteria show that this method can get better clustering effect.

Publisher

Darcy & Roy Press Co. Ltd.

Reference10 articles.

1. Fayyad U, Reina C, Bradley PS: Initialization of iterative refinement clustering algorithm. In:Proc of the Fourth International Conference on Knowledge Discovery and Data Mining. AAAI,Menlo Park. 1998:194-198.

2. L. X. Bang, J. H. Yang, M.G. Wang: Using genetic algorithm to improve K-means clustering algorithm in clustering analysis[J]. Mathematical Statistics and Applied Probability, 1997, 1 2(4):350—356. (In Chinese)

3. Krishma K, Murty MN: Genetic k-means algorithm[J]. IEEE Transactions on System, Man and Cybemetics, Part B. 1999. 29(3): 433-439.

4. Likas, A., Vlassis, N., Jakob, J.V.: The global k-means algorithm algorithm. Pattern Recognition 2003, 36, 451–461.

5. Khan, S.S., Ahmad, A.: Cluster center initialization algorithm for k-means clustering. Pattern Recognition Lett. 2004, 25, 1293–1302.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3