Frequent Moving Pattern Mining Method Using CF-Suffix Trie

Abstract

In this paper, we propose CF-Suffix Trie for mining frequent moving patterns from spatiotemporal data and an online algorithm for constructing the trie. Our methods can discover patterns and their related spatial regions automatically with only a single scan of the data. We evaluate our methods experimentally using datasets with artificial object trajectories. The performance experiment shows that our method are more than 1000 times fater than naive methods and exhibits more than 95 % of precision.