Using a Hash-Based Method for Apriori-Based Graph Mining
Using a Hash-Based Method for Apriori-Based Graph Mining
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DOI：
10.1007/978-3-540-30116-5_33
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发表时间：
2004-09
期刊：
影响因子：
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通讯作者：
P. Nguyen;T. Washio;K. Ohara;H. Motoda
中科院分区：
文献类型：
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作者：
P. Nguyen;T. Washio;K. Ohara;H. Motoda
图数据的频繁子图的发现问题可以通过首先构造子图候选集，然后在该候选集内识别出满足频繁子图要求的子图来解决。在基于Apriori的图挖掘中，从大量生成的邻接矩阵中确定候选子图通常是整体图挖掘性能的主导因素，因为它需要执行许多图同构测试。为了解决这个问题，我们开发了一种有效的候选集生成算法。它是一种基于哈希的算法，通过对现实世界和合成图数据的实验证实其有效。
The problem of discovering frequent subgraphs of graph data can be solved by constructing a candidate set of subgraphs first, and then, identifying within this candidate set those subgraphs that meet the frequent subgraph requirement. In Apriori-based graph mining, to determine candidate subgraphs from a huge number of generated adjacency matrices is usually the dominating factor for the overall graph mining performance since it requires to perform many graph isomorphism tests. To address this issue, we develop an effective algorithm for the candidate set generation. It is a hash-based algorithm and was confirmed effective through experiments on both real-world and synthetic graph data.