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

基于模糊C-均值的空间不确定数据聚类 被引量：7

Clustering of Space Uncertain Data Based on Fuzzy C-means
下载PDF
导出
摘要 针对现实世界中样本对象的不确定性及样本对象间界限划分的模糊性,提出基于模糊C-均值的空间不确定数据聚类算法UFCM。但由于UFCM算法在聚类过程中涉及大量期望距离的复杂积分计算,导致UFCM算法性能不理想,进而给出改进算法I_UFCM,将空间不确定对象聚类问题转化为传统的确定对象聚类问题,采用相似度计算公式减少期望距离的计算量,提高聚类结果的质量。实验结果表明,与UFCM和UK-Means算法相比,I_UFCM算法在空间不确定数据集上具有更好的聚类性能,CUP耗时降低了90%以上。 Aiming at the uncertainty of sample object in real world and the fuzzy boundary between sample objects,this paper proposes a Uncertain Fuzzy C-Means（UFCM）algorithm.Because of a lot of complex integral calculation in expected distance computation,UFCM algorithm is inefficiency.Further,an improved algorithm called I_UFCM is proposed.In this algorithm,the spatial uncertain objects are transformed into the traditional certain objects for clustering.Besides,a new formula for calculation similarity is introduced instead of traditional Euclidean norm to evaluate the distance between objects.The quality of clustering results is improved by reducing the computational amount of excepted distance.Experimental results demonstrate the clustering performance of I_UFCM algorithm is more effective than UFCM and UK-Means algorithm,and its CPU time is reduced by 90%.
作者 肖宇鹏 何云斌 万静 李松
机构地区 哈尔滨理工大学计算机科学与技术学院
出处 《计算机工程》 CAS CSCD 北大核心 2015年第10期47-52,共6页 Computer Engineering
基金 黑龙江省自然科学基金资助项目(F201014 F201134 F201302) 黑龙江省教育厅科学技术研究基金资助项目(12531120 12541128 12511100)
关键词 模糊C-均值 不确定数据 概率密度函数 期望距离 质心 fuzzy C-means uncertain data probability density function excepted distance centroid
分类号 TP18 [自动化与计算机技术—控制理论与控制工程]
  • 相关文献

参考文献15

  • 1Aggarwal C C,Yu P S.A Survey of Uncertain Data Algorithms and Applications[J].IEEE Transactions on Knowledge and Data Engineering,2009,21(5):609-623.
  • 2Jiang Bin,Pei Jian,Tao Yufei,et al.Clustering Uncertain Data Based on Probability Distribution Similarity[J].IEEE Transactions on Knowledge and Data Engineering,2013,25(4):751-763.
  • 3Chau M,Cheng R,Kao B,et al.Uncertain Data Mining:An Example in Clustering Location Data[C]//Proceedings of PAKDD’06.Berlin,Germany:Springer,2006:199-204.
  • 4Kriegel H P,Pfeifle M.Density-based Clustering of Uncertain Data[C]//Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining.New York,USA:ACM Press,2005:672-677.
  • 5Kao B,Lee S D.Clustering Uncertain Data Using Voronoi Diagrams and r-tree Index[J].IEEE Transactions on Knowledge and Data Engineering,2010,22(9):1219-1233.
  • 6Lee S D,Kao B,Cheng R.Reducing UK-means to Kmeans[C]//Proceedings of the 7th IEEE International Conference on Data Mining Workshops.Washington D.C.,USA:IEEE Press,2007:483-488.
  • 7Günnemann S,Kremer H,Seidl T.Subspace Clustering for Uncertain Data[C]//Proceedings of 2010SIAM International Conference on Data Mining.[S.l.]:Society for Industrial and Applied Mathematics,2010:385-396.
  • 8Ngai W K,Kao B,Cheng R,et al.Metric and Trigonometric Pruning for Clustering of Uncertain Data in 2DGeometric Space[J].Information Systems,2011,36(2):476-497.
  • 9Gullo F,Tagarelli A.Uncertain Centroid Based Partitional Clustering of Uncertain Data[J].Proceedings of the VLDB Endowment,2012,5(7):610-621.
  • 10Nazari M,Shanbehzadeh J,Sarrafzadeh A.Fuzzy Cmeans Based on Automated Variable Feature Weighting[C]//Proceedings of International Multi Conference of Engineers and Computer Scientists.Calgary,Canada:International Association of Engineers,2013:13-15.

二级参考文献19

  • 1Basu S, Banerjee A, Mooney R. Semi-supervised Clustering by Seeding[C]//Proc. of the 19th International Conference on Machine Learning. Sydney, Australia: [s. n.], 2002: 19-26.
  • 2Wagstaff K, Cardie C, Rogers S, et al. Constrained K-Means Clustering with Background Knowledge[C]//Proc. of the 18th International Conference on Machine Learning. San Francisco, USA: [s. n.], 2001: 577-584.
  • 3Zhang Daoqiang, Tan Keren, Chen Songcan. Semi-supervised Kernel-based Fuzzy C-Means[C]//Proc. of 2004 International Conference on Neural Information Processing. Calcutta, India: [s. n ], 2004: 1229-1234.
  • 4Grira N, Crncianu M, Boujemaa N. Semi-supervised Fuzzy Clustering with Competitive Agglomera- tion[C]//Proc, of IEEE International Conference on Fuzzy Systems [S. 1.]: IEEE Press, 2005: 867-872.
  • 5Wang Na, Li Xia, Luo Xuehui. Semi-supervised Kernel-based Fuzzy C-Means with Pairwise[C]//Proc. of 2008 International Joint Conference on Neural Networks. [S. 1.]: IEEE Press, 2008: 1098-1101.
  • 6Zhang Daoqiang, Chen Songcan, Zhou Zhihua. Learning the Kernel Parameters in Kernel Minimum Distance Classifier[J]. Pattern Recognition, 2006, 39(1): 133-135.
  • 7Dunn J.C..Some recent investigations of a new fuzzy partition algorithm and its application to pattern classification problems.Journal of Cybernetics,1974,4:1～15
  • 8Bezdek J.C..Pattern Recognition with Fuzzy Objective Function Algorithms.New York:Plenum,1981
  • 9Wang X.Z.,Wang Y.D.,Wang L.J..Improving Fuzzy C-Means clustering based on feature-weight learning.Pattern Recognition Letters,2004,25(10):1123～1132
  • 10Gustafson D.E.,Kessel W..Fuzzy clustering with a fuzzy covariance matrix.In:Proceedings of the IEEE Conference on Decision Control,San Diego,CA,1979,761～766

共引文献45

同被引文献68

引证文献7

二级引证文献31

计算机工程
2015年 第10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