Mining Recurring Events Through Forest Growing

Recurring events are short temporal patterns that consist of multiple instances in the target database. Without any a priori knowledge of the recurring events, in terms of their lengths, temporal locations, the total number of such events, and possible variations, it is a challenging problem to discover them because of the enormous computational cost involved in analyzing huge databases and the difficulty in accommodating all the possible variations without even knowing the target. We translate the recurring event mining problem into finding temporally continuous paths in a matching-trellis. A novel algorithm that simulates a "forest-growing" procedure in the matching-trellis is proposed. Each tree branch in the resulting forest naturally corresponds to a discovered repetition, with temporal and content variations tolerated. By using locality sensitive hashing (LSH) to find best matches efficiently, the overall complexity of our algorithm is only sub-quadratic to the size of ...

• Type: Journal
• Year: 2008
• Where: TCSV
• Authors: Junsong Yuan, Jingjing Meng, Ying Wu, Jiebo Luo
• Keywords: Content Variations, Enormous Computational Cost, Possible Variations, TCSV 2008