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基于实数值链接分析的ESSC融合算法
  • 摘要

    为了进一步提升ESSC聚类融合性能,采用实数值链接分析(real valued link analysis)计算聚类融合中模糊数据类的相似性。根据模糊决策及其相似性定义优化的融合信息,从而达到改进聚类性能的目的。实验选用了两个仿真数据库和五个UCI数据库。实验结果表明,基于实数值链接分析的ESSC聚类融合算法(RLA-ESSCE)的性能优于K-means聚类算法(KMC)、ESSC、ESSCE。

  • 作者

    WANG Li-juan  HAO Zhi-feng  CAI Rui-chu  WEN Wen 

  • 作者单位

    School of Computer Science & Engineering,South China University of Technology,Guangzhou 510006,China/School of Computer Science & Engineering,South China University of Technology,Guangzhou 510006,China;Faculty of Computer,Guangdong University of Technology,Guangzhou 510006,China/Faculty of Computer,Guangdong University of Technology,Guangzhou 510006,China

  • 刊期

    2014年5期 ISTIC PKU

  • 关键词

    增强的软子空间聚类  聚类融合  实数值链接分析  聚类融合信息  ESSC  clustering ensemble  real valued link analysis  clustering ensemble information 

参考文献
  • [1] 张宪超,徐雯,高亮,梁文新. 一种结合文本和链接分析的局部Web社区识别技术. 计算机研究与发展, 2012,11
  • [2] 陈黎飞,郭躬德,姜青山. 自适应的软子空间聚类算法. 软件学报, 2010,10
  • [3] 毕志升,王甲海,印鉴. 基于差分演化算法的软子空间聚类. 计算机学报, 2012,10
  • [4] 王骏,王士同,邓赵红. 特征加权距离与软子空间学习相结合的文本聚类新方法. 计算机学报, 2012,8
  • [5] JIANG Da-xin;TANG Chun;ZHANG Ai-dong. Cluster analysis for gene expression data:a survey. {H}IEEE Transactions on Knowledge and Data Engineering, 2004,11
  • [6] STREHL A;GHOSH J. Cluster ensembles-a knowledge reuse framework for combining multiple partitions. {H}JOURNAL OF MACHINE LEARNING RESEARCH, 2002
  • [7] TOPCHY A;JAIN A K;PUNCH W. Clustering ensembles:models of consensus and weak partitions. {H}IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005,12
  • [8] FERN X Z;BRODLEY C E. Random projection for high dimensional data clustering:a cluster ensemble approach. Washington DC:AAAI Press, 2003
  • [9] NG A;JORDAN M;WEISS Y. On spectral clustering:analysis and an algorithm. Advances in Neural Information Processing Systems, 2001
  • [10] KRIEGEL H;KROGER P;ZIMEK A. Clustering high-dimensional data:a survey on subspace clustering,pattern based clustering,and correlation clustering. ACM Trans on Knowledge Discovery from Data, 2009,01
  • [11] DOMENICONI C;GUNOPULOS D;MA Sheng. Locally adaptive metrics fore clustering high dimensional data. Data Mining Knowledge Discovery, 2007,01
  • [12] JING L P. An entropy weighting K-means algorithm for subspace clustering of high dimensional sparse data. {H}IEEE Transactions on Knowledge and Data Engineering, 2007,08
  • [13] DENG Z H;CHOI K S;CHUNG F L. Enhanced soft subspace clustering integrating within-cluster and between-cluster information. {H}Pattern Recognition, 2010,03
  • [14] ADAMIC L A;ADAR E. Friends and neighbors on the Web. {H}SOCIAL NETWORKS, 2003,03
  • [15] DONOHO D. High-dimensional data analysis:the curses and blessings of dimensionality. 2000
  • [16] PARSONS L;HAQUE E;LIU H. Subspace clustering for high dimensional data:a review. ACM SIGKDD Explorations Newsletter, 2004,01
  • [17] BOONGOEN T;SHEN Q;PRICE C. Disclosing false identity through hybrid link analysis. Artificial Intelligence and Law, 2010,01
  • [18] IAM O N;BOONGOEN T;GARRETT S. A link-based approach to the cluster ensemble problem. {H}IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011,12
  • [19] IAM O N;BOONGOEN T. Improved link-based cluster ensembles. 2012
  • [20] LIU Zong-yi;SARKAR S. Improved gait recognition by gait dynamics normalization. {H}IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006,06
  • [21] CHEN Xiao-jun;YE Yun-ming;XU Xiao-fei. A feature group weighting method for subspace clustering of high-dimensional data. {H}Pattern Recognition, 2012,01
  • [22] DOMENICONI C;Al-RAZGAN M. Weighted cluster ensembles:methods and analysis. ACM Trans on Knowledge Discovery from Data, 2009,04
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