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一种用于生物网络数据的频繁模式挖掘算法 被引量：6

An Algorithm for Frequent Pattern Mining in Biological Networks
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摘要 频繁模式挖掘是生物网络数据分析中的一个核心问题,对于研究生物网络的组织结构和功能模块具有重要意义.本文提出了子图环分布的概念并构造了子图搜索算法,提高了搜索效率.其次设计了动态抽样算法计算子图频率,用于提高非穷举搜索的精度.利用4个真实生物网络数据进行仿真实验研究,验证了本文算法在效率和精度上相对于现有算法的优势. Frequent pattern mining has emerged as a key issue for analyzing the biological networks since it gives us insights into the organism and functional modules.A novel algorithm for this problem is proposed,which can efficiently obtain all these frequent subgraphs in networks based on the distribution of ring.To improve the accuracy of subgraph mining in non-exhaustive enumerate mode,additionally,we provide a dynamic sample algorithm.The experimental results in four real bio-networks show the superiority of our algorithm to existing algorithms.
作者 赵建邦 董安国 高琳
机构地区 西安电子科技大学计算机学院 长安大学理学院
出处 《电子学报》 EI CAS CSCD 北大核心 2010年第8期1803-1807,共5页 Acta Electronica Sinica
基金 国家自然科学基金重点项目(No.60933009) 高等学校博士学科点专项科研基金(No.200807010013)
关键词 生物网络 频繁模式 子图搜索 biological network frequent pattern subgraph search
分类号 TP311.12 [自动化与计算机技术—计算机软件与理论]
作者简介 赵建邦 男，1983年生于陕西渭南，现在西安电子科技大学计算机学院攻读计算机应用技术专业博士学位，研究方向为生物信息数据挖掘．E—mail：zjb9797@foxmail．com 董安国 男，1964年生于浙江象山，西安长安大学教授，硕士生导师，于1987年毕业于西安交通大学数学系．主要从事生物信息学、图论与矩阵论算法及其应用的研究工作．
  • 相关文献

参考文献10

  • 1R Milo,S Shen-Orr,S Itzkovitz,et al.Network motifs:Simple building blocks of complex networks[J].Science,2002,298(5594):824-827.
  • 2A Inokuchi,T Washio,H Motoda.An apriori-based algorithm for mining frequent substructures from graph data .Proc of European Conf on Principles of Data Mining and Knowledge Discovery(PKDD 2000) .London,UK:Springer-Verlag,2000.13-23.
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二级参考文献49

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共引文献6

同被引文献49

  • 1汪卫,周皓峰,袁晴晴,楼宇波,施伯乐.基于图论的频繁模式挖掘[J].计算机研究与发展,2005,42(2):230-235. 被引量：17
  • 2陈晓云,陈袆,王雷,李荣陆,胡运发.基于分类规则树的频繁模式文本分类[J].软件学报,2006,17(5):1017-1025. 被引量：19
  • 3刘勇,李建中,朱敬华.一种新的基于频繁闭显露模式的图分类方法[J].计算机研究与发展,2007,44(7):1169-1176. 被引量：10
  • 4Deshpande M, Kuramochi M, Karypis G. Frequent substructure based approaches for classifying chemical compounds [ J ]. IEEE Trans on Knowledge and Data Engineering,2005,17(8) :1 036-1 050.
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  • 8Yan X, Han J. Closegraph : Mining closed frequent graph patterns [ C ]//Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining(KDD). Washington DC, USA:ACM,2003:286-295.
  • 9Liu B, Hsu W, Ma Y. Integrating classification and association role mining[ C ]//Proceedings of the Fourth International Con- ference on Knowledge Discovery and Data Mining(KDD-98). New York, USA: AAAI, 1998:80-86.
  • 10Silva A, Meira Jr W, Zaki M J. Structural correlation pattern mining for large graphs [ C ]//Proceedings of the Eighth Workshop on Mining and Learning with Graphs. USA : ACM, 2010 : 119-126.

引证文献6

二级引证文献24

电子学报
2010年 第8期

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