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SA-BFSN:一种自适应基于密度聚类的算法 被引量：3

SA-BFSN:adaptive algorithm based on density clustering
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摘要 针对BFSN算法需要人工输入参数r和λ的缺陷,提出了一种自适应确定r和λ的SA-BFSN聚类方法。该方法通过Inverse Gaussian拟合判断r参数,通过分析噪声点数量的分布特征选择合适的λ值。算法测试表明,使用SA-BFSN无需人工输入参数,能够实现聚类过程的全自动化,能够有效处理任意形状、大小和密度的簇。 Algorithm for BFSN defects that require manual input parameters r and λ, an adaptive SA-BFSN clustering method that can automatically determine r and λ is proposed. The method determines r by Inverse Gaussian fitting parameters, and by analyzing the distribution of the number of noise points to select the appropriate value of λ. Algorithm tests show that use of SA-BFSN doesn＇t need human input parameters, to achieve full automation of the clustering process, to deal effectively with any shape, size and density of the cluster.
作者 陈昊 侯慧群 杨承志 邱磊
机构地区 空军航空大学
出处 《计算机工程与应用》 CSCD 2012年第36期186-189,共4页 Computer Engineering and Applications
关键词 数据挖掘 密度聚类 基于广度优先搜索邻居的聚类算法(BFSN) 自适应基于广度优先搜索邻居的聚 类算法(SA-BFSN) data mining density clustering Broad First Search Neighbors （BFSN） Self-Adaptive Broad First Search Neighbors（SA-BFSN）
分类号 TP391 [自动化与计算机技术—计算机应用技术]
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参考文献8

  • 1侯慧群.雷达对抗原理[M].长春:空军第二航空学院出版社,2001..
  • 2祝正威.雷达信号的聚类分选方法[J].电子对抗,2005(6):6-10. 被引量：33
  • 3岳宏伟,罗景青,吕久明,叶菲.雷达信号非均匀粒度聚类分选方法[J].火力与指挥控制,2008,33(8):23-26. 被引量：6
  • 4石陆魁,何丕廉.一种基于密度的高效聚类算法[J].计算机应用,2005,25(8):1824-1826. 被引量：21
  • 5Dana C, Simovici D A.An information-theoretical approach to clustering categorical databases using genetic algo- rithms[C]//Proceedings of the Workshop on Clustering High Dimensional Data and Its Applications, Washington, 2002: 37-46.
  • 6Kaufman L,Rousseeuw P J.Finding groups in data:an introduction to cluster analysis[M].New York:Wiley Press, 2005.
  • 7Lin C Y, Chang C C, Lin C C.A new density-based scheme for clustering based on genetic algorithm[J].Fun- damenta Informaticae, 2005,68 (4) : 315-331.
  • 8Halkidi M,Vazirgiannis M.Clustering validity assessment: finding the optimal partitioning of a data set[C]//Cercone N, Lin T Y, Wu Xindong.Proceedings of the 2001 IEEE International Conference on Data Mining.California: IEEE Computer Society,2001.

二级参考文献17

  • 1徐峰,张铃.基于商空间的非均匀粒度聚类分析[J].计算机工程,2005,31(3):26-28. 被引量：14
  • 2KAUFMAN L, ROUSSEEUW PJ. Finding groups in data: An introduction to cluster analysis[ M]. New York: John Wiley & Sons,1990.
  • 3RAYMOND T NG, HAN JW. CLARANS: A method for clustering objects for spatial data mining[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2002, 14(5): 1003 -1016.
  • 4ZHANGT, RAMAKRISHNAN R, LIVNY M. BIRCH: An efficient data clustering method for very large databases[ A]. Proceedings of the ACM SIGMOD internatioal conference on Management of data[C]. New York: ACM Press, 1996. 103 - 114.
  • 5GUHA S, RASTOGI R, SHIM K. CURE: An efficient clustering algorithm for large databases[ A]. Proceedings of the ACM SIGMOD internatioal conference on Management of data[ C]. New York: ACM Press, 1998.73 - 84.
  • 6ESTER M, HANS-PETER KRIEGEL, SANDER J, et al. A densitybased algorithm for discovering clusters in large spatial databases with noise[A]. Proceeding the 2nd international conference on Knowledge discovery and data mining(KDD) [ C]. Portland, 1996.226 - 231.
  • 7ANKERST M, BREUNIG M, HANS-PETER KRIEGEL, et al. OPTICS: Ordering points to identify the clustering structure[ A]. Proceedings of the ACM SIGMOD internatioal conference on Management of data[ C]. New York: ACM Press, 1999.49 -60.
  • 8WANG W, YANG J, MUNTZ R. STING: A statistical information grid approach to spatial data mining[ A]. Proceedings of the 23rd IEEE international conference on very large data bases[ C]. Athens,1997. 186 - 195.
  • 9AGRAWAL R, GEHRKE J, GUNOPULOS D, et al. Automatic subspace clustering of high dimensional data for data mining applications[A]. Proceedings of the ACM SIGMOD internatioal conference on Management of data[ C]. New York: ACM Press, 1998.94 - 105.
  • 10BECKMANN N, HANS-PETER KRIEGEL, SCHNEIDER R, et al.The R * - Tree: An efficient and robust access method for points and rectangles[ A]. Proceedings of the ACM SIGMOD conference on Management of data[ C]. New York: ACM Press, 1990.322 -331.

共引文献58

同被引文献33

  • 1张敏,于剑.基于划分的模糊聚类算法[J].软件学报,2004,15(6):858-868. 被引量：176
  • 2陈佐,谢赤,陈晖.基于小波聚类方法的股票收益率序列时间模式挖掘[J].系统工程,2005,23(11):102-107. 被引量：5
  • 3赵光嗣,李伟生.基于MPI的并行小波聚类算法在曙光TC1700上的实现[J].计算机应用,2006,26(3):645-646. 被引量：2
  • 4王玲,薄列峰,焦李成.密度敏感的半监督谱聚类[J].软件学报,2007,18(10):2412-2422. 被引量：94
  • 5Deufemia V, Risi M, Tortora G. Sketached symbol recognition using latent-dynamic conditional random fields and distance-based clustering[J]. Pattern Recognition, 2014, 47(3): 1159-1171.
  • 6Portela N M, Cavalcanti G, Ren T I. Semi-supervised clustering for MR brain image segmentation[J]. Expert Systems with Applications, 2014, 41(4) : 1492- 1497.
  • 7Voevodski K, Balcan M F, R6glin H, et al. Active clustering of biological sequences[J]. Journal of Machine Learning Research, 2012, 13, 203-225.
  • 8Rudi L. A fast quartet tree heuristic for hierarchical clustering[J]. Pattern Recognition, 2011, 44 (3): 662-677.
  • 9Su M C, Chou C H. A modified version of the k-means algorithm with distance based on cluster symmetry[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(6): 674-680.
  • 10Celebi M E, Kingravi H A, Vela P A. A comparative study of efficient initialization methods for the k- means clustering algorithm[J]. Expert Systems with Applications, 2013, 40(1): 200-210.

引证文献3

二级引证文献10

计算机工程与应用
2012年 第36期

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