摘要
研究不确定图数据的挖掘,主要解决不确定图数据的频繁子图模式挖掘问题.介绍了一种数据模型来表示图的不确定性,以及一种期望支持度来评价子图模式的重要性.利用期望支持度的Apriori性质,给出了一种基于深度优先搜索策略的挖掘算法.该算法使用高效的期望支持度计算方法和搜索空间裁剪技术,使得计算子图模式的期望支持度所需的子图同构测试的数量从指数级降低到线性级.实验结果表明,该算法比简单的深度优先搜索算法快3-5个数量级,有很高的效率和可扩展性.
This paper studies uncertain graph data mining and especially investigates the problem of mining frequent subgraph patterns from uncertain graph data. A data model is introduced for representing uncertainties in graphs, and an expected support is employed to evaluate the significance of subgraph patterns. By using the apriori property of expected support., a depth-first search-based mining algorithm is proposed with an efficient method for computing expected supports and a technique for pruning search space, which reduces the number of subgraph isomorphism testings needed by computing expected support from the exponential scale to the linear scale. Experimental results show that the proposed algorithm is 3 to 5 orders of magnitude faster than a naive depth-first search algorithm, and is efficient and scalable.
出处
《软件学报》
EI
CSCD
北大核心
2009年第11期2965-2976,共12页
Journal of Software
基金
国家自然科学基金Nos.60533110
60773063
国家重点基础研究发展计划(973)No.2006CB303005
新世纪优秀人才支持计划No.NCET-05-0333~~
关键词
不确定图
图挖掘
频繁子图模式
uncertain graph
graph mining
frequent subgraph pattern
