Two efficient algorithms for mining high utility sequential patterns

High utility sequential pattern mining (HUSPM) is an emerging topic in data mining. Compared with the previous topics (sequential pattern mining and high utility itemset mining), HUSPM can provide more applicable knowledge, for it comprehensively considers utility indicating the business value and sequential indicating the causality of different items. However, the combination of utility and sequential brings the dramatic challenges and makes HUSPM more difficult than the previous problems. In this paper, we propose an two efficient algorithms, HUS-UT and HUS-Par, for HUSPM. The proposed IRIS-UT algorithm adopts a novel data structure named Utility-Table to facilitate the utility calculation, so it can find the desired patterns quickly. The HUS-Par algorithm is a parallel version of HUS-UT based on the thread model, which also exploits two balance strategies to improve efficiency. We also conduct substantially experiments to evaluate the performance of our algorithms. The experimental results show that our algorithms are much faster than the state-of-the-art algorithms.