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首页> 外文期刊>Knowledge and Data Engineering, IEEE Transactions on >Mining Frequent Subgraph Patterns from Uncertain Graph Data
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Mining Frequent Subgraph Patterns from Uncertain Graph Data

机译：从不确定的图数据中挖掘频繁的子图模式

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摘要

In many real applications, graph data is subject to uncertainties due to incompleteness and imprecision of data. Mining such uncertain graph data is semantically different from and computationally more challenging than mining conventional exact graph data. This paper investigates the problem of mining uncertain graph data and especially focuses on mining frequent subgraph patterns on an uncertain graph database. A novel model of uncertain graphs is presented, and the frequent subgraph pattern mining problem is formalized by introducing a new measure, called expected support. This problem is proved to be NP-hard. An approximate mining algorithm is proposed to find a set of approximately frequent subgraph patterns by allowing an error tolerance on expected supports of discovered subgraph patterns. The algorithm uses efficient methods to determine whether a subgraph pattern can be output or not and a new pruning method to reduce the complexity of examining subgraph patterns. Analytical and experimental results show that the algorithm is very efficient, accurate, and scalable for large uncertain graph databases. To the best of our knowledge, this paper is the first one to investigate the problem of mining frequent subgraph patterns from uncertain graph data.
机译：在许多实际应用中，图形数据由于数据的不完整和不精确性而具有不确定性。挖掘此类不确定的图形数据在语义上与挖掘常规精确图形数据相比在计算上更具挑战性。本文研究了不确定图数据的挖掘问题，尤其着重于在不确定图数据库中挖掘频繁的子图模式。提出了一种新颖的不确定图模型，并通过引入一种称为期望支持的新方法，将频繁子图模式挖掘问题形式化。事实证明，这个问题很难解决。提出了一种近似挖掘算法，通过允许发现子图模式的预期支持上的容错来找到一组近似频繁的子图模式。该算法使用有效的方法来确定是否可以输出子图模式，并使用一种新的修剪方法来降低检查子图模式的复杂性。分析和实验结果表明，该算法对于大型不确定图数据库非常有效，准确且可扩展。据我们所知，本文是第一个研究从不确定图数据中挖掘频繁子图模式问题的人。

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