期刊文献+

一种有效的多关系聚类算法 被引量:1

Efficient Multi-Relational Clustering Algorithm
下载PDF
导出
摘要 研究多关系数据挖掘的聚类问题,提出一种有效的多关系聚类算法EMC.EMC算法的目标是提高聚类的准确率,并且降低运行时间.EMC算法首先利用元组ID传播的思想,计算两个对象之间的相似度,接着利用K中心点聚类算法,将对象划分成簇.实验表明,EMC算法显著降低运行时间,并且提高聚类的准确率. The problem of clustering in multi-relation data mining was investigated, and an efficient multi-relational clustering algorithm called EMC was proposed. EMC aims at increasing the accuracy of clustering, and decreasing running time. First, EMC computed the similarity between two objects by taking advantage of tuple ID propagation approach. Then, EMC clustered the objects by K-medoids clustering algorithm. Performance results demonstrate that, EMC significantly decreases running time, and increases the accuracy of clustering.
出处 《微电子学与计算机》 CSCD 北大核心 2016年第4期133-137,共5页 Microelectronics & Computer
基金 国家自然科学基金(61462008 61472254 61420106010)
关键词 多关系数据挖掘 聚类 元组ID传播 相似度 K中心点聚类算法 multi-relational data mining clustering tuple ID propagation similarity K-medoids clustering algo- rithm
  • 相关文献

参考文献10

  • 1Ling P, Rong X. Double-phase locality sensitive hashing of neighborhood development for multi-relational[C]// Pro- ceedings of 2014 UK Workshop on Computational Intelli- gence Hong Kong. UKCI, 2014: 206-213.
  • 2Houshmand M, Alishahi M. Improve the classification and Sales management of products using multi-rela- tional data mining[C]// IEEE 3rd International Con-ference on Communication Software and Networks. Xi" an: IEEE, 2011: 329-337.
  • 3Vaghela V, Vandra K, Moni N. MR-MNBC. maxrel based feature selection for the multi-relational naive bayesian classifier[C]//Nirma University Internation- al Conference on Engineering. Ahmedabad NUiCONE, 2013: 1-9.
  • 4Yin X, Han J, Yu P. Cross-relational clustering with users guidance[C]// Proceedings of the llth ACM SIGKDD Conference on Knowledge Discovery in Data Mining. Chicago: ACM, 2005 : 344-353.
  • 5Yin X, Han J, Yu P. LinkClus: efficient clustering via heterogeneous semantic links[C]// Proceedings of International Conference on Very Large Data Bases. Seoul: ACM , 2006: 427-438.
  • 6Han J, Kamber M. Data mining., concepts and tech- niques [M]. FAN M, MENG X, translated. 2nd ed. Beijing. China Machine Press, 2007. 373-382.
  • 7Dehaspe L, Raedt L. Mining association rules in mul- tiple relations[C]// Proceedings of the 7th Interna- tional Workshop on Inductive Logic Programming. Berlin: Springer, 1997: 125-132.
  • 8Nijssen S, Kok J. Faster association rules for multiple relations[C]// Proceedings of 17th International Joint Conference on Artificial Intelligence. Adelaide ACM, 2001: 891-896.
  • 9Lavrac N, Dzeroski S. Inductive logic programming: techniques and applications [M]. New York: Ellis Horwood, 1994.
  • 10Ley M. The DBLP computer science bibliography[DB/ OL]. [2015-04-20] (2014-07-22). http://www, infor- matik, uni-trier, de/ley/dh/.

同被引文献5

引证文献1

二级引证文献1

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