摘要
已提出很多图分类方法。这些方法在挖掘频繁子图时,只考虑了子图的结构信息,没有考虑子图的嵌入信息。实际上,有些频繁子图挖掘算法在计算子图的支持度时,可以获得嵌入信息。在L-CCAM子图编码的基础上,提出了一种基于嵌入集的图分类方法。该方法采用基于类别信息的特征子图选择策略,充分利用嵌入集,在频繁子图挖掘过程中直接选择特征子图。通过实验表明,该方法是有效的、可行的。
Many graph classification approaches have been proposed.These approaches only look at the structural information of the pattern,but do not take advantage of the embedding information during mining frequent subgraph.In facts,in some efficient subgraph mining algorithms,the embedding information of a pattern can be maintained.A graph classification approach was presented.Based on L-CCAM coding,it uses a feature subgraph selection strategy based on label information to select the feature subgraph,while making full use of embedding sets to directly generate feature subgraph in mining frequent subgraph.Experiment results show that it is effective and feasible.
出处
《计算机科学》
CSCD
北大核心
2012年第6期155-158,共4页
Computer Science
关键词
频繁子图
图分类
图挖掘
特征选择
Frequent subgraph
Graph classification
Graph mining
Feature selection
