Mining Frequent Closed Patterns using Sample-growth in Resource Effectiveness Data

As the occurrence of failure of electronic resources is sudden, real-time record analysis on the effectiveness of all resources in the system can discover abnormal resources earlier and start using backup resources or restructure resources in time, thus managing abnormal situations and finally realizing health management of the system. This paper proposed an algorithm: MFPattern, for mining frequent closed resource patterns in resource effectiveness matrix. In order to improve the efficiency, MFPattern algorithm uses samplegrowth method and effective pruning strategies to guarantee mining all frequent closed patterns without candidate maintenance. Different from the traditional frequent closed pattern, MFPattern algorithm can mine resource combination patterns with all resources very effectively during work, those with simultaneous failure of resources and combination patterns in which some resources are very effective while some others have failure. The experimental result shows that our algorithm is more effective than existing algorithms.