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基于粗糙集的数据聚类方法研究 被引量：3

Research on Data Clustering Algorithm Based on Rough Sets
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摘要 RoughSets理论是一种新型的处理含糊和不确定性知识的数学工具,将RoughSets理论应用于知识发现中的聚类分析,给出了信息系统的约简、信息系统的可辨识属性矩阵和信息系统的辨识公式等定义,在此基础上提出了基于粗糙集的数据聚类算法RSDC,实验结果验证了该算法的可行性,并且对符号属性和数值属性数据都具有良好的聚类效果。 Rough sets theory is a new mathematical tool to deal with vagueness and uncertainty.It is applied to clustering analysis in knowledge discovery.A lot of definitions such as reduction of information system,the discernibility matrix of information system,the discernibility formula of information system are given.Based on these definitions,a rough sets-based data clustering algorithm RSDC is proposed.The experimental results show that the algorithm is feasible and has good clustering performance for data of symbolic attributes and numerical attributes.
作者 孙士保 赵文涛 秦克云 王艳丽
机构地区 西南交通大学理学院智能控制开发中心 河南理工大学计算机科学与技术学院 西南交通大学信息科学与技术学院
出处 《计算机工程与应用》 CSCD 北大核心 2006年第22期140-142,共3页 Computer Engineering and Applications
基金 国家自然科学基金资助项目(编号:60474022)
关键词 ROUGH SETS 属性约简 聚类分析 簇 Rough Sets, attribute reduction, clustering analysis, cluster
分类号 TP183 [自动化与计算机技术—控制理论与控制工程]
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参考文献10

  • 1Han J W,Kanber M.Data Mining Concepts and Techniques[M].San Francisco,CA:Morgan Kaufmann Publishers,2001:223～262
  • 2MacQueen J.Some methods for classification and analysis of multivariate observations[C].In:LeCam L M,Neyman J eds.Proc of the 5th Berkeley SYMP on Mathematics,Statics and Probability,SYMP1967,Berkeley:Berkeley University of California Press,1967; 1:281～298
  • 3Kaufmann L,Rousseeuw P J.Finding Groups in Data:An Introduction to Cluster Analysis[M].New York:Lohn Wiley & Sons,1990:67～89
  • 4Ester M,Kriegel H P,Sander J et al.A density-based algorithm for discovering clusters in large spatial databases with noise[C].In:Simoudis E,Han J W,Fayyad U eds.Proc of the 2th Int'l Conf on Knowledge Discovery and Data Mining,KDD96,Menlo Park:AAAI Press,1996:226～231
  • 5Ankerst M,Breuing M,Kriegel H P et al.OPTICS:Ordering points to identify the clustering structure[C].In:Delis A,Faloutsos C,Ghandeharizadeh S eds.Proc of the 1999 ACM SIGMOD Int'l Conf on Management of Data,1999 ACM SIGMOD,New York:ACM Press,1999:46～60
  • 6Wang W,Yang J,Muntz R.STING:A statistical information grid approach to spatial data mining[C].In:Proc of 1997 Int Conf Very Large Data Basks,VLDB'97,Athens,Greece:Springer,1997:186～195
  • 7余建桥,张帆.基于数据场改进的PAM聚类算法[J].计算机科学,2005,32(1):165-167. 被引量：15
  • 8刘少辉,胡斐,贾自艳,史忠植.一种基于Rough集的层次聚类算法[J].计算机研究与发展,2004,41(4):552-557. 被引量：18
  • 9刘少辉,盛秋戬,吴斌,史忠植,胡斐.Rough集高效算法的研究[J].计算机学报,2003,26(5):524-529. 被引量：271
  • 10Hettich S,Blake C L,Merz C J.UCI Repository of machine learning databases.http://www.ics.uci.edu/～mlearn/MLRepository.html,1998

二级参考文献27

  • 1王珏,苗夺谦,周育健.关于Rough Set理论与应用的综述[J].模式识别与人工智能,1996,9(4):337-344. 被引量：264
  • 2[2]Jianwei Han, M Kamber. Data Mining: Concepts and Techniques. San Francisco: Morgan Kaufmann Publishers, 2000
  • 3[3]J Grabmeier, A Rudolph. Techniques of cluster algorithms in data mining. Data Mining and Knowledge Discovery, 2002, 6(4): 303～360
  • 4[4]A K Jain, M N Murty, P J Flynn. Data clustering: A review. ACM Computing Surveys, 1999, 31(3): 264～323
  • 5[5]J MacQueen. Some methods for classification and analysis of multivariate observations. In: L M Le Cam, J Neyman eds. Proc of the 5th Berkeley Symp on Mathematics, Statics and Probability, Vol 1. Berkeley: Berkeley University of California Press, 1967. 281～298
  • 6[6]J C Bezdek. Pattern Recognition with Fuzzy Objective Function Algorithms. New York: Plenum Press, 1981
  • 7[7]L Kaufman, P J Rousseeuw. Finding Groups in Data: An Introduction to Cluster Analysis. New York: John Wiley & Sons, 1990
  • 8[8]M Ester, H P Kriegel, J Sander et al. A density-based algorithm for discovering clusters in large spatial databases with noise. In: E Simoudis, J Han, U Fayyad eds. Proc of the 2nd Int'l Conf on Knowledge Discovery and Data Mining (KDD-96). Menlo Park: AAAI Press, 1996, 226～231
  • 9[9]M Ankerst, M Breuning, H P Kriegel et al. OPTICS: Ordering points to identify the clustering structure. In: A Delis, C Faloutsos, S Ghandeharizadeh eds. Proc of the 1999 ACM SIGMOD Int'l Conf on Management of Data. New York: ACM Press, 1999. 49～60
  • 10[10]W Wang, J Wang, R Muntz. STING: A statistical information grid approach to spatial data mining. In: M Jarke, M J Carey, K R Dittrich et al eds. Proc of the 23rd IEEE Int'l Conf on Very Large Data Bases. San Francisco: Morgan Kaufmann Publishers, 1997, 186～195

共引文献298

同被引文献48

引证文献3

二级引证文献16

计算机工程与应用
2006年 第22期

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