期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

基于有权重超图模型的离群点发现 被引量：1

Research on Outlier Testing Method based on Weighted Hypergraph-model
下载PDF
导出
摘要 结合基于有权重支持度框架的关联规则挖掘方法和基于超图模型的离群点检测方法,给出了一种离群数据的改进定义,并通过一个简单的实例说明了这种离群数据的离群含义,且与原离群点定义做了比较,分析了新定义离群数据的应用价值. Outlier detection is a branch of Data Mining, and it has a wide application at present. This paper presents an approach to detect outlier. The approach is based on weighted association rule mining using weighted support and significant framework and multilevel hypergraph partitioning algorithms. This paper improves the definition of outlier of Finding Outliers in High - Dimensional Space. We test the approach in a real dataset and compare it with the original one.
作者 李威 李宏岩 米守防
机构地区 大连民族学院计算机科学与工程学院
出处 《大连民族学院学报》 CAS 2006年第5期45-48,共4页 Journal of Dalian Nationalities University
关键词 数据挖掘 离群点 超图模型 权重 data mining outlier hypergraph model weight
分类号 TP274 [自动化与计算机技术—检测技术与自动化装置]
  • 相关文献

参考文献9

  • 1Fayyad U M,Smyth P.From Data Mining to Knowledge Discovery:An Overview[C]//In Advances in Knowledge Discovery and Data Mining.AAA1 Press and the MIT Press,1996.
  • 2Hawkins D.Identification of Outliers[M].London:Chapman and Hall,1980.
  • 3Tukey J W.Exploratory Data Analysis[M].MA:Addison-Wesley,1977.
  • 4Preparata F,Shamos M.Computational geometry:an introduction[M].New York:Springer-Verlag,1988.
  • 5Knorr E M,Ng R T.Algorithms for mining distance-based outliers in large datasets[C]//Gupta A,Shmueli,O,Widom J.Proceedings of the 24th International Conference on Very Large Data Bases.New York:Morgan Kaufmann,1998.
  • 6Knorr E M,Ng R T.Finding intensional knowledge of distance-based outliers[C]//Atkinson M P,Orlowska M E,Valduriez P.Proceedings of the 25th International Conference on Very Large Data Bases.Edinburgh,Scotland:Morgan Kaufmann,1999.
  • 7魏藜,宫学庆,钱卫宁,周傲英.高维空间中的离群点发现[J].软件学报,2002,13(2):280-290. 被引量：44
  • 8Feng Tao,Fionn Murtagh,Mohsen Farid.Weighted Association Rule Mining using Weighted Support and Significance Framework[J].UK,2003,35(2):256-264.
  • 9Karypis G,Kumar V.Multilevel k-way hypergraph partitioning[C]//Proceedings of the Design and Automation Conference,1999.

二级参考文献27

  • 1Fayyad, U., Piatetsky-Shapiro, G., Smyth, P. Knowledge discovery and data mining: towards a unifying framework. In: Simoudis, E., Han, J., Fayyad, U.M., eds. Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining. Portland, Oregon: AAAI Press, 1996. 82～88.
  • 2Ng, R. T., Han, J. Efficient and effective clustering methods for spatial data mining. In: Bocca, J.B., Jarke, M., Zaniolo, C., eds. Proceedings of the 20th International Conference on Very Large Data Bases. Santiago: Morgan Kaufmann, 1994. 144～155.
  • 3Ester, M., Kriegel, H.-p., Sander, J., et al. A density-based algorithm for discovering clusters in large spatial databases with noise. In: Simoudis, E., Han, J., Fayyad, U.M., eds. Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining. Portland, Oregon: AAAI Press, 1996. 226～231.
  • 4Zhang, T., Ramakrishnan, R., Linvy, M. BIRCH: an efficient eata clustering method for very large databases. In: Jagadish, H.V., Mumick, I.S., eds. Proceedings of the ACM SIGMOD International Conference on Management of Data. Montreal: ACM Press, 1996. 103～114.
  • 5Wang, W., Yang, J., Muntz, R. STING: a statistical information grid approach to spatial data mining. In: Jarke, M., Carey, M.J., Dittrich, K.R., et al., eds. Proceedings of the 23rd International Conference on Very Large Data Bases. Athens, Greece: Morgan Kaufmann, 1997. 186～195.
  • 6Sheikholeslami, G., Chatterjee, S., Zhang, A. WaveCluster: a multi-resolution clustering approach for very large spatial databases. In: Gupta, A., Shmueli, O., Widom, J., eds. Proceedings of the 24th International Conference on Very Large Data Bases. New York : Morgan Kaufmann, 1998. 428～439.
  • 7Hinneburg, A., Keim, D.A. An efficient approach to clustering in large multimedia databases with noise. In: Agrawal, R., Stolorz, P.E., Piatetsky-Shapiro, G. eds. Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining. New York: AAAI Press, 1998. 58～65.
  • 8Agrawal, R., Gehrke, J., Gunopulos, D., et al. Automatic subspace clustering of high dimensional data for data mining applications. In: Haas, L.M., Tiwary, A., eds. Proceedings of the ACM SIGMOD International Conference on Management of Data. Seattle, Washington, D C: ACM Press, 1998. 94～105.
  • 9Ruts, I., Rousseeuw, P. Computing depth contours of bivariate point clouds. Journal of Computational Statistics and Data Analysis, 1996,23:153～168.
  • 10Arning, A., Agrawal, R., Raghavan, P. A linear method for deviation detection in large databases. In: Simoudis, E., Han, J., Fayyad, U.M., eds. Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining. Portland, Oregon: AAAI Press, 1996. 164～169.

共引文献43

同被引文献20

引证文献1

二级引证文献1

大连民族学院学报
2006年 第5期

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