基于频繁模式树的频繁连通闭图集挖掘算法  

An Algorithm for Mining Connected Closed Frequent Subgraphs Based on FP-Tree

作　　者：刘振[1] 杨路明[1] 彭佳扬[1] 

机构地区：[1]中南大学信息科学与工程学院,湖南长沙410083

出　　处：《计算机技术与发展》2009年第5期37-40,44,共5页Computer Technology and Development

基　　金：湖南省科学研究项目(08b040)

摘　　要：随着频繁模式挖掘的深入研究,图模型被广泛地应用于为各种事务建模,因此图挖掘的研究显得越来越重要。文中针对唯一标识的有向连通图模型,基于频繁模式树结构,改进了频繁模式增长算法挖掘频繁连通闭合子图。使用生物代谢路径数据集的实验证明,这种算法能有效地挖掘出唯一标识的有向连通图集中的频繁闭图集,一次运算可以挖掘出多个阈值的最大频繁子图集。这种算法适用于以唯一标识的有向连通图建模的网络或图集,可以应用到基于图简化模型的生物网络的子图挖掘任务中。With the deep study of the frequent pattern mining, graphs can be modeled for many transactions widely, and the study of graphs have become increasingly important. Based on FP- Tree, presents an improved FP- Growth algorithm, which can find the closed frequent connected subgraph from the model of unique labeled directed connected graph set. The experiment of biology metabolize pathway dataset demonstrated that the algorithm can get the closed frequent subgraph set effectively, and can get the max frequent suhgraph sets of many different threshold by execute once. This algorithm can use for mining the network or graph set which can modeling by unique labeled, directed, connected graph. It can be applied to the subgraph mining in biological networks which is based on the simplification model.

关 键 词：子图挖掘 频繁模式树 频繁模式增长 频繁闭图集 生物网络 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]