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Efficient Frequent Subgraph Mining in Transactional Databases

机译:事务数据库中的有效频繁子图挖掘

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Frequent connected subgraph mining (FCSM) has been an active area of research over the last twenty years. This review shall focus on the practical and theoretical issues arising in the transactional setting, where we are given a finite list of small to medium sized graphs and must find all graphs that are subgraph isomorphic to some user-defined number of graphs in the list. In particular, we present the generic approach to FCSM and investigate sufficient conditions for its computational tractability and intractability. Interestingly, it turns out that both depend on the complexity of the Hamiltonian Path problem. This implies that FCSM is computationally tractable only for very restricted transaction graph classes. We subsequently review existing exact FCSM algorithms with a focus on applicability to arbitrary graph databases and present recent approximative FCSM algorithms that remain computationally tractable for all transactional databases.
机译:过去二十年来,频繁连接子图挖掘(FCSM)一直是研究的活跃领域。这次审查将集中在交易环境中出现的实践和理论问题上,在这里,我们获得了中小型图形的有限列表,并且必须找到与该列表中一些用户定义的图形同构的子图形的所有图形。特别是,我们介绍了FCSM的通用方法,并研究了其计算易处理性和难处理性的充分条件。有趣的是,事实证明两者都取决于汉密尔顿路径问题的复杂性。这意味着FCSM仅对于非常受限的事务图类在计算上是可处理的。随后,我们重点介绍了适用于任意图形数据库的现有精确FCSM算法,并提出了对于所有事务数据库在计算上仍然易于处理的最新近似FCSM算法。

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