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ZTE Communications ›› 2017, Vol. 15 ›› Issue (S2): 43-46.DOI: 10.3969/j.issn.1673-5188.2017.S2.007

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  • 收稿日期:2017-07-28 出版日期:2017-12-25 发布日期:2020-04-16

An Improved K-Means Algorithm Based on Initial Clustering Center Optimization

LI Taihao1,2, NAREN Tuya1, ZHOU Jianshe1, REN Fuji3, LIU Shupeng4   

  1. 1. Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing 100048, China
    2. Flatley Discovery Lab, Boston 02129, USA
    3. Department of Information Science & Intelligent Systems, University of Tokushima, Tokushima 7708506, Japan
    4. School of Communication and Information Engineering, Shanghai University, Shanghai 200072, China
  • Received:2017-07-28 Online:2017-12-25 Published:2020-04-16
  • About author:LI Taihao (litaihao@heartdynamic.cn) received the M.S. and Ph.D. degrees in information system engineering from University of Tokushima, Japan in 2003 and 2006, respectively. During 2006-2011, he was a postdoc researcher at Harvard University, USA. He is now a professor with Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, China. His research interests include affective computing, natural language processing, and artificial intelligence.|NAREN Tuya (nrty0910@163.com) received her M.S. degree from Jilin University, China. She is a Ph.D. student at Capital Normal University, China. Her research interests include language intelligence, linguistics, and text affective computing.|ZHOU Jianshe (zhoujianshe@solcnu.net) is a board member of Chinese Linguistics Association, a director member of Expert Committee of the Linguistics Committee of Beijing, and the deputy director of Beijing Linguistics Association. He is the vice president of Capital Normal University, China and a professor with Beijing Advanced Innovation Center for Imaging Technology there. His research interests include linguistics, neuroscience, and algorithms.|REN Fuji (ren@is.tokushima-u.ac.jp) received his B.E. and M.E. degrees from Beijing University of Posts and Telecommunications, China in 1982 and 1985, respectively. He received his Ph.D. degree in 1991 from Hokkaido University, Japan. He is a professor at the Faculty of Engineering, the University of Tokushima, Japan. His research interests include natural language processing, affective computing, artificial intelligence, and language understanding.|LIU Shupeng (liusp@i.shu.edu.cn) received his Ph.D. degree in 2007 from Shanghai Jiaotong University, China. He is an associate professor with School of Communications and Information Engineering, Shanghai University, China. His research interests include signal processing, Raman spectra, and image processing.

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Abstract:

The K-means algorithm is widely known for its simplicity and fastness in text clustering. However, the selection of the initial clustering center with the traditional K-means algorithm is some random, and therefore, the fluctuations and instability of the clustering results are strongly affected by the initial clustering center. This paper proposed an algorithm to select the initial clustering center to eliminate the uncertainty of central point selection. The experiment results show that the improved K-means clustering algorithm is superior to the traditional algorithm.

Key words: clustering, K-means algorithm, initial clustering center