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利用局部图关联信息挖掘加权频繁模式耿汝年 董祥军 张 萍 须文波


  摘 要:遍历模式数据挖掘方法已经在多种应用中被提出,传统的遍历模式挖掘仅仅考虑了非加权遍历。为解决加权遍历模式挖掘问题,首先提出了一种从ewdg(边加权有向图)到vwdg(顶点加权有向图)的变换模型;基于这种模型,提出了在具有层次特性的局部图遍历中,挖掘加权频繁模式的lgtwfpminer(局部图遍历加权频繁模式挖掘法)及其支持度/权值界的局部评估方法。针对合成数据的实验结果表明该算法能够有效地进行基于图遍历的加权频繁模式挖掘。

  关键词:数据挖掘;加权有向图;遍历模式;频繁模式;支持度界

  中图分类号:tp311 文献标志码:a

   文章编号:1001-3695(2008)09-2687-05

  mining weighted frequent patterns using local graph linking information

  geng runian1,2,dong xiangjun2,zhang ping1,3, xu wenbo1

  (1.school of information technology, jiangnan university, wuxi jiangsu 214122, china;2.school of information science & technology, shandong institute of light industry, jinan 250353, china;3.china ship scientific research center, wuxi jiangsu 214082, china)abstract:data mining for traversal patterns has been found useful in several applications. however, traditional model of traversal patterns mining only considered unweighted traversals.this paper proposed a transformable model of ewdg (edgeweighted directed graph) and vwdg (vertexweighted directed graph)to resolve the problem of weighted traversal patterns mining. based on the model,developed a new algorithm,called lgtwfpminer(local graph traversalsbased weighted frequent patterns miner), and its local estimation of support/weightbound to discover weighted frequent patterns from the traversals on graph with a level property. experimental results of synthetic data show the algorithm is effective to resolve the problem of mining weighted frequent patterns based on graph traversals. 

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