期刊文献+

一种基于加权欧氏距离聚类方法的研究 被引量:17

下载PDF
导出
摘要 针对传统的基于欧氏距离计算相似度的不足,提出了一种在领域知识未知的情况下基于加权欧氏距离的计算方法,并对此进行了分析与研究。实验证明,该方法不仅在一定程度上克服了欧氏距离的缺陷,而且能够提高聚类质量,优化聚类性能。
出处 《计算机应用》 CSCD 北大核心 2006年第S2期152-153,共2页 journal of Computer Applications
基金 内蒙古自治区高等教育科学研究项目(NJ04019)
  • 相关文献

参考文献3

二级参考文献44

  • 1贺仲雄.模糊数学及其应用[M].天津科学技术出版社,1982,4.1-320.
  • 2高建.中国企业技术创新分析[M].清华大学出版社,1999..
  • 3[1]Fasulo, D. An analysis of recent work on clustering algorithms. Technical Report, Department of Computer Science and Engineering, University of Washington, 1999. http://www.cs.washington.edu.
  • 4[2]Baraldi, A., Blonda, P. A survey of fuzzy clustering algorithms for pattern recognition. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 1999,29:786~801.
  • 5[3]Keim, D.A., Hinneburg, A. Clustering techniques for large data sets - from the past to the future. Tutorial Notes for ACM SIGKDD 1999 International Conference on Knowledge Discovery and Data Mining. San Diego, CA, ACM, 1999. 141~181.
  • 6[4]McQueen, J. Some methods for classification and Analysis of Multivariate Observations. In: LeCam, L., Neyman, J., eds. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability. 1967. 281~297.
  • 7[5]Zhang, T., Ramakrishnan, R., Livny, M. BIRCH: an efficient data clustering method for very large databases. In: Jagadish, H.V., Mumick, I.S., eds. Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data. Quebec: ACM Press, 1996. 103~114.
  • 8[6]Guha, S., Rastogi, R., Shim, K. CURE: an efficient clustering algorithm for large databases. In: Haas, L.M., Tiwary, A., eds. Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data. Seattle: ACM Press, 1998. 73~84.
  • 9[7]Beyer, K.S., Goldstein, J., Ramakrishnan, R., et al. When is 'nearest neighbor' meaningful? In: Beeri, C., Buneman, P., eds. Proceedings of the 7th International Conference on Data Theory, ICDT'99. LNCS1540, Jerusalem, Israel: Springer, 1999. 217~235.
  • 10[8]Ester, M., Kriegel, H.-P., Sander, J., et al. A density-based algorithm for discovering clusters in large spatial databases with noises. In: Simoudis, E., Han, J., Fayyad, U.M., eds. Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD'96). AAAI Press, 1996. 226~231.

共引文献420

同被引文献102

引证文献17

二级引证文献55

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部