A LFP-tree based method for association rules mining in telecommunication alarm correlation analysis

The mining of association rules is one of the primary methods used in telecommunication alarm correlation analysis, of which the alarm databases are very large. The efficiency of the algorithms plays an important role in tackling with large datasets. The classical frequent pattern growth (FP-growth) algorithm can produce a large number of conditional pattern trees which made it difficult to mine association rules in telecommunication environment. In this paper, an algorithm based on layered frequent pattern tree (LFP-tree) is proposed for mining frequent patterns. Efficiency of this algorithm is achieved with following techniques: 1) All the frequent patterns are condensed into a layered structure, which can save memory occupied. The layered structure can not only reduce the mining time but also be very useful for updating the alarm databases. 2) Each alarm item can be viewed as a triple < a, v, t >, in which t is a Boolean variable that shows the item frequent or not. 3) Deleting infrequent items with dynamic pruning can avoid produce conditional pattern sets. Simulation and analysis of algorithm show that it is a valid method with better time and space efficiency, which is adapted to mine association rules in telecommunication alarm correlation analysis.