通信网告警相关性分析中有效的关联规则挖掘算法

关联规则挖掘算法是通信网告警相关性分析中的重要方法。在处理数量庞大的告警数据库时，算法的效率显得至关重要，而经典的FP-growth算法会产生大量的条件模式树，使得在通信网环境下挖掘关联规则的难度非常大。针对上述问题，提出了一种基于分层频繁模式树的LFPTDP算法，采用分层模式树的方法产生频繁项集，从而避免了产生大量的条件模式树，并用动态剪枝的方法删除大量的非频繁项。算法分析及仿真表明，LFPTDP算法具有较好的时间和空间效率，是一种适合于通信网告警相关性分析的关联规则挖掘算法。

An efficient method for association rules mined in telecommunication alarm correlation analysis

The mining of association rules is one of the primary methods used in telecommunication alarm correlation analysis, in which the alarm databases are very large. The efficiency of the algorithms plays an important role in tackling large datasets. The classical FP-growth algorithm can produce a large number of conditional pattern trees which makes it difficult to mine association rules in telecommunication environment. In this paper, an algorithm LFPTDP based on the Layered Frequent Pattern Tree is proposed for mining frequent patterns and deleting infrequent items with dynamic pruning which can avoid producing conditional pattern trees. Analysis and simulation show that it is a valid method with better time and space efficiency, which is adapted to mining association rules in telecommunication alarm correlation analysis.