期刊文献+

基于图论的EMST聚类算法的适用性研究

Study on The Applicability of EMST Clustering Algorithm based on Graph Theory
下载PDF
导出
摘要 针对空间聚类算法EMST在实际应用中适用性不清,具有很大局限性的问题,提出了通过对比实验来确定EMST算法在空间数据库中的性能的方法。实验从簇的空间形态(类球形和任意形状)、簇的空间密度差异、簇的空间邻近和噪声点(孤立点)的影响来分析EMST算法的优缺点,同时分析不同参数对EMST算法聚类结果的影响。通过实验分析总结,提高了EMST算法的实际应用价值。 Aiming at the problem of unclear applicability and great limitation of spatial clustering algorithm EMST in practical application,a method is proposed to determine the performance of EMST algorithm in spatial database through comparative experiments.In this experiment,the advantages and disadvantages of EMST algorithm were analyzed from the aspects of spatial morphology(spherical and arbitrary shapes)of clusters,spatial density difference of clusters,spatial proximity of clusters and the influence of noise points(outludes),as well as the influence of different parameters on the clustering results of EMST algorithm.Through experimental analysis and summary,the practical application value of EMST algorithm is improved.
作者 魏亮 陈晓耀 崔亚涛 WEI Liang;CHEN Xiao-yao;CUI Ya-tao(Bozhou Institute of Chinese Medicine,Anhui Academy of Chinese Medicine,Anhui Bozhou 236800;Bozhou Vocational and Technical College,Anhui Bozhou 236800;Hefei University of Technology,Anhui Hefei 230009)
出处 《贵阳学院学报(自然科学版)》 2020年第1期69-71,97,共4页 Journal of Guiyang University:Natural Sciences
基金 安徽省2018年度高等学校省级质量工程项目(项目编号:2018jyxm0283) 亳州职业技术学院2018年度科研项目(项目编号:BYK1807)。
关键词 EMST 适用性 空间聚类 算法 EMST Applicability Spatial clustering Algorithm
  • 相关文献

参考文献8

二级参考文献54

  • 1郎显宇,陆忠华,迟学斌.一种基于“基因表达谱”的并行聚类算法[J].计算机学报,2007,30(2):311-316. 被引量:11
  • 2MACQUEEN J. Some Methods for Classification and A nalysis of Multivariate Observations [C]//Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability. Berkeley: University of California Press, 1967: 281-297.
  • 3KAUFMAN L, ROUSSEEUW P J. Finding Groups in Data: An Introduction to Cluster Analysis[M]. New York: John Wiley &Sons, 1990.
  • 4ZHANG T, RAMAKRISHNAN R, LIVNY M. BIRCH: An Efficient Data Clustering Method for Very Large Databases[C]//Proceedings of the International Conference Management of Data. Montreal: ACM Press, 1996: 103-114.
  • 5GUHA S, RASTOGI R, SHIM K. CURE: An Efficient Clustering Algorithm for Large Databases [C]//Proceed ings of 1998 ACM SIGMOD International Conference on Management of Data (SIGMOD'98). Seattle:ACM Press, 1998:73- 84.
  • 6GUHA S, RASTOGI R, SHIM K. ROCK: A Robust Clustering Algorithm for Categorical Attributes [C]//Pro ceedings of the International Conference of Data Engineering (ICDE ' 99). Washington: IEEE Computer Society, 1999: 512 -521.
  • 7ESTER M, KRIEGEL H P, SANDER J, et al. A Density- based Algorithm for Discovering Clusters in Large Spatial Databases with Noise[C]//Proceedings of the 2nd Interna tional Conference on Knowledge Discovery and Data Mining. Portland: AAAI Press, 1996: 226-231.
  • 8ANKERST M, BREUNIG M , KRIEGEL H P, et al. OP- TICS: Ordering Points to Identify the Clustering Structure [C]//Proceedings of the 1999 ACM-SIGMOD International Conference on Management of Data (DIGMOD' 99). New York: ACM Press, 1999: 49-60.
  • 9HINNEBURG A, KEIM D A. An Efficient Approach to Clustering in Large Multimedia Databases with Noise [C]//Proceedings of the 1998 International Conference on Knowledge Discovery and Data Mining (KDD' 98). New York: ACM Press, 1998.
  • 10WANG W, YANG J, MUNTZ R. STING.. A Statistical Information Grid Approach to Spatial Data Mining [C]// Proceedings of the 1997 International Conference on Very Large Data Bases (VLDB' 97). San Francisco: Morgan Kaufmann Publishers, 1997 : 186-195.

共引文献53

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