Mining Sequential Patterns from Probabilistic Databases

We consider sequential pattern mining in situations where there is uncertainty about which source an event is associated with. We model this in the probabilistic database framework and consider the problem of enumerating all sequences whose expected support is sufficiently large. Unlike frequent itemset mining in probabilistic databases [C. Aggarwal et al. KDD'09; Chui et al., PAKDD'07; Chui and Kao, PAKDD'08], we use dynamic programming (DP) to compute the probability that a source supports a sequence, and show that this suffices to compute the expected support of a sequential pattern. Next, we embed this DP algorithm into candidate generate-and-test approaches, and explore the pattern lattice both in a breadth-first (similar to GSP) and a depth-first (similar to SPAM) manner. We propose optimizations for efficiently computing the frequent 1-sequences, for re-using previously-computed results through incremental support computation, and for elmiminating candidate sequences without computing their support via probabilistic pruning. Preliminary experiments show that our optimizations are effective in improving the CPU cost.