Mining Closed Multi-Dimensional Interval Patterns

Many methods have been proposed to find frequent one-dimensional (1-D) interval patterns, where each event in the database is realized by a 1-D interval. However, the events in many applications are in nature realized by multi-dimensional intervals. Therefore, in this paper, we propose an efficient algorithm, called MIAMI, to mine closed multi-dimensional interval patterns from a database. The MIAMI algorithm employs a pattern tree to enumerate all closed patterns in a depth-first search manner. In the mining process, we devisethree effective pruning strategies to remove impossible candidates and perform a closure checking scheme to eliminate non-closed patterns. The experimental results show that the MIAMI algorithm is more efficient and scalable than the modified Apriori algorithm.