论文标题
路径关联规则挖掘
Path association rule mining
论文作者
论文摘要
图形结合规则挖掘是一种用于发现图数据规律性的数据挖掘技术。在这项研究中，我们提出了一个新颖的概念，即{\ IT路径关联规则挖掘}，以发现经常出现在给定图中的路径模式的相关性。在我们的概念中应用了可达性路径模式（即存在从顶点到另一个顶点的路径的存在）以发现各种规律性。我们证明了问题是NP-HARD，并且我们开发了一种有效的算法，其中抗单调性质用于路径模式。随后，我们开发出近似和并行化技术，以有效且可扩展地发现规则。我们使用现实生活图来实验验证有效的
Graph association rule mining is a data mining technique used for discovering regularities in graph data. In this study, we propose a novel concept, {\it path association rule mining}, to discover the correlations of path patterns that frequently appear in a given graph. Reachability path patterns (i.e., existence of paths from a vertex to another vertex) are applied in our concept to discover diverse regularities. We show that the problem is NP-hard, and we develop an efficient algorithm in which the anti-monotonic property is used on path patterns. Subsequently, we develop approximation and parallelization techniques to efficiently and scalably discover rules. We use real-life graphs to experimentally verify the effective
