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二阶段模糊聚类方法研究

Research on two-stage fuzzy clustering method
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摘要 针对当前聚类方法存在的缺点,提出一种高效的高维数据硬划分算法,在此基础上提出了一种分阶段模糊聚类方法.第一阶段,利用硬划分算法对数据聚类,克服了模糊聚类算法对初始值敏感的缺点.第二阶段,以第一阶段运算结果作为初始值,进行模糊聚类的,并将模拟退火算法引入模糊聚类,从而保证了聚类结果的全局最优性.实验结果表明,该方法是可行的、有价值的. A novel high-dimensional clustering algorithm is proposed. On the basis of this, a two-stage fuzzy clustering approach, named TFC, is presented. The first stage clusters data by a new clustering method. The second stage, the result of the first stage, is taken as the initial cluster centers, and the simulated annealing mechanism is induced into fuzzy clustering to solve the locality and the sensitiveness of the initial condition of Fuzzy C-means Clustering. The running results of the system show that it is feasible and valuable to apply this method to mining the outlier in spectrum data.
出处 《哈尔滨工程大学学报》 EI CAS CSCD 北大核心 2006年第B07期552-557,共6页 Journal of Harbin Engineering University
基金 国家“863”高技术研究发展计划基金资助项目(2003AA133060)山西省自然科学基金资助项目(2006011041).
关键词 模糊聚类 模拟退火 恒星光谱数据 全局最优 fuzzy clustering simulated annealing star optical spectrum data global optimization
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参考文献14

  • 1KAMBR J H.Data mining concepts and techniques[M].[s.l.] Morgan Kaufmann Publishers,2000.
  • 2KARYPIS G,HAN E H,KUMAR V.Chameleon:a hierarchical clustering algorithm using dynamic modeling[R].Technical Report,#99-007,1999.
  • 3GUHA S,RASTOGI R,SHIM K.Cure:an efficient clustering algorithm for large databases[A].In:Proc of the ACM SIGMOD Int'l Conf on Management of Data[C].Seattle:ACM Press,1998.
  • 4ZHANG T,RAMAKRISHNAN R,LIVNY M.Birch:an efficient data clustering method for very large databases[A].Proc.of the 1996 ACM SIGMOD Int'lConf.on Management of Data[C].Montreal,1996.
  • 5FAN M.Data mining concepts and techniques[M].Beijing:China Machine Press,2001.
  • 6ORDONEZ C,OMIECINSKI E.Frem:fast and robust EM clustering for large data sets[A].In:Proc.of the 2002 ACM CIKM Int'l Conf.on Information and Knowledge Management[C].[s.l.],2002.
  • 7HINNEBURG A,KEIM D.An efficient approach to clustering in large multimedia databases with noise[A].In:Proc.of the 4th Int'l Conf.on Knowledge Discovery and Data Mining (KDD'98)[C].New York:AAAI Press,1998.
  • 8ANKERST M,BREUNIG M M,KRIEGEL H P,SANDER J.Optics:ordering points to identify the clustering structure[A].In:Proc ACM SIGMOD Int'lConf on Management of Data[C].Philadelphia,1999.
  • 9ESTER M,KRIEGEL H,SANDER J,XU X W.A density-based algorithm for discovering clusters in large spatial databases with noise[A].In:Proc of the 2nd Int'l Conf on Knowledge Discovery and Data Mining (KDD'96)[C].Portland,1996.
  • 10宋擒豹,沈钧毅.基于关联规则的Web文档聚类算法[J].软件学报,2002,13(3):417-423. 被引量:41

二级参考文献9

  • 1[1]Broder,A.Z.,Glassman,S.C.,Manasse,M.S.Syntactic clustering of the Web.Technical Report,1997-015,Palo Alto,CA:Digital Systems Research Center (Digital),1997.
  • 2[2]Chang,C.H.,Hsu,C.C.Customizable multi-engine search tool with clustering.Computer Network and ISDN Systems,1997,29(8-13):1217~1224.
  • 3[3]Chen,L.,Katya,S.Webmate:a personal agent browsing and searching.In:Sycara,K.P.,Wooldridge,M.,eds.Proceedings of the 2nd International Conference on Autonomous Agents.New York:ACM Press,1998.132~139.
  • 4[4]Ron,W.,Bienvenido,V.,Mark,A.S.,et al.Hypursuit:a hierarchical network search engine that exploits content-link hypertext clustering.In:ACM,ed.Proceedings of the 7th ACM Conference on Hypertext.New York:ACM Press,1996.180~193.
  • 5[5]Ackerman,M.,Billsus,D.,Gaffney,S.,et al.Learning probabilistic user profiles.AI Magazine,1997,18(2):47~56.
  • 6[6]Cheeseman,P.,Stutz,J.Bayesian classification (autoclass):theory and results.In:Fayyad,U.M.,Piatetsky-Shapiro,G.,Smyth,P.,et al.,eds.Advances in Knowledge Discovery and Data Mining.Menlo Park,CA:AAAI/MIT Press,1996.153~180.
  • 7[7]Agrawal,R.,Srikant,R.Fast algorithm for mining association rules.In:Jorge,B.B,Matthias,J.,Carlo,Z.,eds.Proceedings of the 20th International Conference on Very Large Databases.Santiago:Morgan Kaufmann Publishers,Inc.,1994.487~499.
  • 8宋擒豹,沈钧毅.基于关联规则的Web文档聚类算法[J].软件学报,2002,13(3):417-423. 被引量:41
  • 9钱卫宁,周傲英.从多角度分析现有聚类算法(英文)[J].软件学报,2002,13(8):1382-1394. 被引量:86

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