期刊文献+

基于空间同位模式的贝叶斯网构建

Learning a Bayesian network from spatial co-location pattern
原文传递
导出
摘要 空间同位挖掘方法是空间数据挖掘中挖掘空间点要素之间潜在关联规则的方法,通过对空间同位挖掘算法和贝叶斯网的分析,提出了一种从空间同位挖掘算法构建贝叶斯网的方法.该方法主要通过把空间同位挖掘算法中挖掘出的空间同位作为贝叶斯网中的结点变量,并把挖掘过程中定义的支持度作为条件概率,构建出一个基于空间同位模式的叶斯网,给出了算法和实验.利用贝叶斯网可以构建出关于空间同位模式的全概率空间. spatial co-location rule is a way to discovery potential pattern in spatial database,this paper analysed spatial co-location discovery algorithms and Bayesian networks,proposed a method to learning a Bayesian networks form spatial co-location pattern.this method mainly consider a co-location pattern as the node in Bayesian network meanwhile seem condition probability in spatial co-location pattern as the condition probability in Bayesian networks.They build a Bayesian networks bases on spatial co-location pattern and give the algorithms and experiment of learning Bayesian networks from spatial co-location pattern.
出处 《云南大学学报(自然科学版)》 CAS CSCD 北大核心 2010年第S2期5-12,共8页 Journal of Yunnan University(Natural Sciences Edition)
关键词 空间同位 贝叶斯网 空间推理 co-location Bayesian network spatial reasoning
  • 相关文献

参考文献7

  • 1慕春棣,tsinghua.edu.cn,戴剑彬,叶俊.用于数据挖掘的贝叶斯网络[J].软件学报,2000,11(5):660-666. 被引量:100
  • 2C. Agostinelli,R. Rotondi.Using Bayesian belief networks to analyse the stochastic dependence between interevent time and size of earthquakes[J]. Journal of Seismology . 2003 (3)
  • 3David Heckerman.Bayesian Networks for Data Mining[J]. Data Mining and Knowledge Discovery . 1997 (1)
  • 4David Heckerman,Dan Geiger,David M. Chickering.Learning Bayesian Networks: The Combination of Knowledge and Statistical Data[J]. Machine Learning . 1995 (3)
  • 5Ralf Hartmut Güting Dr.rer.nat.An introduction to spatial database systems[J]. The VLDB Journal . 1994 (4)
  • 6Shashi Shekhar,Sanjay Chawla.Spatial database:atour. . 2 005
  • 7Philip M Dixon.Ripley’’s k function. http://www.stat.iastate.edu/preprint/articles/2001-18.pdf . 2001

二级参考文献11

  • 11.Chickering D. Learning equivalence classes of Bayesian networks structures. In: Horvitz E, Jensen F ed. Proceedings of the 12th Conference on Uncertainty in Artificial Intelligence. San Francisco, CA: Morgan Kaufmann Publishers, Inc., 1996. 54~61
  • 22.Geriger D, Hekerman D. A charactererization of the Dirichlet distribution with application to learning Bayesian networks. In: Besnard P, Hanks S eds. Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann Publishers Inc., 1995. 196~207
  • 33.Heckman D. A Bayesian approach for learning causal networks. In: Besnard P, Hanks S eds. Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence. San Francisco, CA: Morgan Kaufmann Publishers, Inc., 1995. 285~295
  • 44.Heckman D, Geiger D, Chickering D. Learning Bayesian networks: the combination of knowledge and statistical data. Machine Learning, 1995,20(3):197~243
  • 55.Heckman D, Shachter R. Decision-Theoretic foundations for causal reasoning. Journal of Artificial Intelligence Research, 1995,3:405~430
  • 66.Heckman D, Mandani A, Wellman M. Real-World applications of Bayesian networks. Communications of the ACM, 1995,38(3):38~45
  • 77.Buntine W. Theory refinement on Bayesian networks. In: Proceedings of the 7th Conference on Uncertainty in Artificial Intelligence. Los Angeles, CA: Morgan Kaufmann Publishers, Inc., 1991. 52~61
  • 88.Cooper G, Herskovits E. A Bayesian method for the introduction of probabilistic networks from data. Machine Learning, 1992,9(4):309~347
  • 99.Russell S, Binder J, Koller D et al. Local learning in probabilistic networks with hidden variables. In: Cooper G F, Moral S ed. Proceedings of the 14th International Joint Conference on Artificial Intelligence. San Francisco, CA: Morgan Kaufmann Publishers, Inc., 1998. 1146~1152
  • 101999-03-15

共引文献99

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部