Temporal data mining with up-to-date pattern trees

Mining interesting and useful frequent patterns from large databases attracts much attention in recent years. Among the mining approaches, finding temporal patterns and regularities is very important due to its practicality. In the past, Hong et al. proposed the up-to-date patterns, which were frequent within their up-to-date lifetime. Formally, an up-to-date pattern is a pair with the itemset and its valid corresponding lifetime in which the user-defined minimum support threshold must be satisfied. They also proposed an Apriori-like approach to find the up-to-date patterns. This paper thus proposes the up-to-date pattern tree (UDP tree) to keep the up-to-date 1-patterns in a tree structure for reducing database scan. It is similar to the FP-tree structure but more complex due to the requirement of up-to-date patterns. The UDP-growth mining approach is also designed to find the up-to-date patterns from the UDP tree. The experimental results show that the proposed approach has a better performance than the level-wise mining algorithm.