期刊文献+

图像数据挖掘方法研究

a Study on the Methods of Graph-Based Data Mining
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摘要 对基于图的数据挖掘国内外研究现状作了总结,从相关的基本概念开始,介绍了目前最具代表的三个算法AGM、FSG和gSpan的主要思想,并对各算法的复杂性作了比较分析和论述,并对图挖掘将来的发展方向作了简要分析。 This article introduces the related theoretical basis of graph based data mining and surveys the state of the art of graph based data mining. The main ideas of the three typical algorithms AGM, FSG, gSpan are described in detail, and the complexities of the algorithms are analyzed and compared with each other. Brief analysis of the future research directions is provided as well.
出处 《科技广场》 2007年第11期61-64,共4页 Science Mosaic
关键词 图挖掘 频繁子图 子图同构 规范标识 Graph Mining Frequent Subgraph Subgraph Isomorphism Canonical Labeling
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参考文献2

  • 1Luc Dehaspe,Hannu Toivonen. Discovery of frequent DATALOG patterns[J] 1999,Data Mining and Knowledge Discovery(1):7~36
  • 2Kenichi Yoshida,Hiroshi Motoda,Nitin Indurkhya. Graph-based induction as a unified learning framework[J] 1994,Applied Intelligence(3):297~316

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