Pruning Redundant Alarm Correlation Patterns

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Pruning Redundant Alarm Correlation Patterns

Efficient methods exist for discovering association rules from large collections of data. The number of discovered rules can, however, be so large. At the same time it is well known that many discovered associations are redundant or minor variations of others. Their existence may simply be due to chance rather than true correlation. Thus, those spurious and insignificant rules should be removed. In this paper, we propose a novel technique to overcome this problem. The technique firstly introduces the new concept -- structure rule cover, and then present a quantitative method to prune redundant correlation patterns. The user can now obtain a complete picture of the domain without being overwhelmed by a huge number of rules.