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基于信息熵的空间对象群聚类算法 被引量:2

Clustering Algorithm for Spatial Object Group Based on Information Entropy
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摘要 针对利用空间关系建立空间对象群聚类的问题,提出一种基于信息熵的空间对象群聚类算法ESOGC。该算法考虑空间数据的复杂性和数据之间的联系,根据邻域范围内信息熵的变化情况,捡起或放下当前空间对象群,从而实现对空间对象群的聚类。实验结果表明,该算法能解决空间对象群中对象类型、对象属性值和对象数量不一致性的问题。 For the clustering of spatial object group constructed based on spatial relationship,this paper presents a clustering algorithm for spatial object group based on information entropy,named ESOGC.ESOGC is different from the other clustering algorithms,and it takes variety data types and the number of objects into full account in spatial object group.Through the change of information entropy within a same region,ants determine whether to pick up or drop the current spatial object group to realize the clustering of spatial object group.Experimental results show it can solve the problems of different data types,attribute value,and number.
出处 《计算机工程》 CAS CSCD 北大核心 2011年第16期176-178,181,共4页 Computer Engineering
基金 国家自然科学基金资助项目(40871176)
关键词 空间对象群 空间关系 聚类 信息熵 蚁群算法 spatial object group spatial relationship clustering information entropy ant colony algorithm
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  • 1周水庚,周傲英,金文,范晔,钱卫宁.FDBSCAN:一种快速 DBSCAN算法(英文)[J].软件学报,2000,11(6):735-744. 被引量:42
  • 2佟兆廷,张果,许辉熙.我国城市人力资本投资类型及空间分布研究[J].四川师范大学学报(自然科学版),2005,28(3):354-357. 被引量:3
  • 3Ester M, Kriegel H P, Sander J, et al. A Density-based Algorithm for Discovering Clusters in Large Spatial Databases with Noise[C]//Proc. of the 2nd Int'l Conf. on Knowledge Discovering in Databases and Data Mining. Massachusetts, USA: AAAI Press, 1996.
  • 4Martin E, Hans P, Sander K J. Incremental Clustering for Mining in a Data Warehousing Environment[C]//Proc. of the 24th Int'l Conf. on VLDB. New York, USA: Morgan Kaufmann Publishers Inc., 1998.
  • 5NG R T,HAN J.CLARANS:a method for clustering objects for spatial data mining[J].IEEE Transactions on Knowledge and Data Engineering,2002,5 (14):1003-1016.
  • 6ESTER M,KRIEGEL H P.A density-based algorithm for discovering clustering in large spatial databases with noise[C]//Proceeding of 2nd KDD.Piscataway:IEEE Press,1996:226-231.
  • 7SANDER J,ESTER M,KRIEGEL H P.Density-based clustering in spatial databases:the algorithm GDBSCAN and its applications[J].Data Mining and Knowledge Discovery,1998,2(6):169-194.
  • 8WANG Xin,HAMILTON H J.DBRS:a density-based spatial clustering method with random sampling[C]//Advances in Knowledge Discovery and Data Mining.7th Pacific-Asia Conference,PAKDD 2003.Heidelberg:SpringerVerlag,2003:563-575.
  • 9HU Cai-ping,QIN Xiao-lin.A novel spatial cluster algorithm with sampling[C]//TORRA V,NARUKAWA Y,YOSHIDA Y.MDAI 2007,LNAI 4617.Berlin Heidelberg:Springer Verlag,200?:216-225.
  • 10GRUVER O U.GRIDBSCAN:GRId density-based spatial clustering of applications with noise[J].Systems,Man and Cybernetics,2006,4 (8):2976-2981.

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