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蚁群优化与模糊聚类结合的文本聚类研究 被引量:3

Research on document clustering based on ant colony combined with Fuzzy C-means
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摘要 针对模糊文本聚类算法(FCM)对输入顺序以及初始点敏感的问题,提出了一种使用蚁群优化的模糊聚类算法(FACA)。该算法采用蚁群聚类算法(ACA)找到聚类的初始中心点,以解决模糊聚类的输入顺序以及初始点敏感等问题。模糊文本聚类算法的线性复杂度使其更便于在计算机实现。与经典的基本模糊聚类以及蚁群聚类在真实数据集上仿真相比较,结果表明经蚁群优化过的模糊聚类算法(FACA)效果更有效,更适合应用于大型的数据集。 Focusing on the problem that the Fuzzy C-Means clustering algorithm is sensitive to initial centers and input order,a document clustering algorithm combined with ant colony clustering and Fuzzy C-Means is proposed.The algorithm takes advantages of ant colony clustering algorithm to find the initial centers,then uses Fuzzy C-Means to get the accurate result.Experimental results show the good performance of the hybrid document clustering algorithm,and it is better for the large-sized dataset.
出处 《计算机工程与应用》 CSCD 北大核心 2010年第32期126-129,共4页 Computer Engineering and Applications
基金 河南省科技攻关项目(No.072102210013)~~
关键词 文本聚类 模糊聚类(FCM) 蚁群聚类(ACA) 蚁群优化的模糊聚类算法(FACA) document clustering; Fuzzy C-Means clustering algorithm; Ant Colony clustering Algorithm; Ant Colony clustering algorithm combined with Fuzzy C-menas;
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  • 1陈浩,何婷婷,姬东鸿.基于k-means聚类的无导词义消歧[J].中文信息学报,2005,19(4):10-16. 被引量:16
  • 2Regina Barzilay,Min-Yen Kan,and Kathleen R.McKeown.Simfinder:A Flexible Clustering Tool for Summarization[A].In proceedings of the Workshop on Summarization in NAACL 01[C].Pittsburg,Pennsylvania,USA:June 2001.
  • 3Zheng Chen,Wei-Ying Ma,Jinwen Ma.Learning to Cluster Web Search Results[A].In:proceedings of the 27th Annual International ACM SIGIR Conference[C].Sheffield,South Yorkshire,UK,July 2004,210 -217.
  • 4Y.C.Fang,S.Parthasarathy,F.Schwartz.Using Clustering to Boost Text Classification[J].In:proceedings of the IEEE ICDM Workshop on Text Mining,Maebashi City,Japan,2002.
  • 5A.Rauber,and M.Frühwirth.Automatically Analyzing and Organizing Music Archives[A].In:proceedings of the 5.European Conference on Research and Advanced Technology for Digital Libraries (ECDL 2001)[C].Darmstadt,Germany,2001.
  • 6Cutting,D.,Karger,D.,and etc.Scatter/Gather:A Cluster-based Approach to Browsing Large Document Collections[A].SIGIR ‘ 92,1992[C].318-329.
  • 7JR Wen,JY Nie,HJ Zhang.Clustering User Queries of a Search Engine[A].The Tenth International World Wide Web Conference[C].Hong Kong.May 1 -5,2001.
  • 8Anton Leuski and James Allan.Improving Interactive Retrieval by Combining Ranked Lists and Clustering[A].In:proceedings of RIAO2000[C].Paris,France,April 12-14,2000,665 -681.
  • 9Anton V.Leouski and W.Bruce Croft.An Evaluation of Techniques for Clustering Search Results[A].Technical Report IR-76,Department of Computer Science,University of Massachusetts,Amherst,1996.
  • 10Htttp://www.cs.washington.edu/research/clustering.

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  • 1刘远超,王晓龙,徐志明,关毅.文档聚类综述[J].中文信息学报,2006,20(3):55-62. 被引量:65
  • 2陈健美,宋顺林,陆虎,宋余庆,朱玉全.改进模糊聚类算法及其在入侵检测中的应用[J].东南大学学报(自然科学版),2007,37(4):589-592. 被引量:12
  • 3张云,冯博琴,麻首强,刘连梦.蚁群-遗传融合的文本聚类算法[J].西安交通大学学报,2007,41(10):1146-1150. 被引量:15
  • 4陈庆奎.基于强化学习的多机群网格资源调度模型[J].计算机科学,2007,34(11):67-70. 被引量:5
  • 5Taherdangkooa M, Bagheri M H. A powerful hybrid clustering method based on modified stem cells and fuzzy C-means algo- rithms [ J ]. Engineering Applications of Artificial Intelligence, 2013,26 (5-6) : 1493-1502.
  • 6Yao Jingtao, Vasilakos A V, Pedrycz W. Granular computing : perspectives and challenges [ J ]. IEEE Trans on Cybernetics, 2013,43(6) :1977-1989.
  • 7Huang Bing, Zhuang Yuliang, Li Huaxiong. Information granu- lation and uncertainty measures in interval-valued intuitionis- tic fuzzy information systems [ J ]. European Journal of Opera-2012,50(8) :3242-3255.
  • 8Liu Hongbing, Xiong Shengwu, Wu Changan. Hyperspherical granular computing classification algorithm based on fuzzy lat- tices[ J ]. Mathematical and Computer Modelling, 2013,57 : 661-670.
  • 9Smith C B, Agaian S, Akopian D. A wavelet-denoising ap- proach using polynomial threshold operators [ J ]. IEEE Signal Processing Letters ,2008,15:906-909.
  • 10Niknam T, Amiri B. An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis [ J ]. Applied Soft Computing, 2010, ! 0 : 183 - 197.

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