从图数据库中挖掘频繁跳跃模式

很多频繁子图挖掘算法已被提出.然而,这些算法产生的频繁子图数量太多而不能被用户有效地利用.为此,提出了一个新的研究问题:挖掘图数据库中的频繁跳跃模式.挖掘频繁跳跃模式既可以大幅度地减少输出模式的数量,又能使有意义的图模式保留在挖掘结果中.此外,跳跃模式还具有抗噪声干扰能力强等优点.然而,由于跳跃模式不具有反单调性质,挖掘它们非常具有挑战性.通过研究跳跃模式自身的特性,提出了两种新的裁剪技术:基于内扩展的裁剪和基于外扩展的裁剪.在此基础上又给出了一种高效的挖掘算法GraphJP(an algorithm for mining jump patterns from graph databases).另外,还严格证明了裁剪技术和算法GraphJP的正确性.实验结果表明,所提出的裁剪技术能够有效地裁剪图模式搜索空间,算法GraphJP是高效、可扩展的.

Mining Frequent Jump Patterns from Graph Databases

Many algorithms on subgraph mining have been proposed. However, the number of frequent subgraphs generated by these algorithms may be too large to be effectively explored by users, especially when the support threshold is low. In this paper, a new problem of mining frequent jump patterns from graph databases is proposed. Mining frequent jump patterns can dramatically reduce the number of output graph patterns and still capture interesting graph patterns. Futhermore, jump patterns are robust against noise and dynamic changes in data. However, this problem is challenging due to the underlying complexity associated with frequent subgraph mining as well as the absence of Apriori property for jump patterns. By exploring the properties of jump patterns, two novel effective pruning techniques are proposed: Internal-Extension-Based pruning and external-extension-based pruning. Based on the proposed pruning techniques, an efficient algorithm GraphJP is presented for this new problem. It has been theoretically proven that the novel pruning techniques and the proposed algorithm are correct. Extensive experimental results demonstrate that the novel pruning techniques are effective in pruning the unpromising parts of search space, and GraphJP is efficient and scalable in mining frequent jump patterns.