Permutation Strategies for Mining Significant Sequential Patterns The identification of significant patterns, defined as patterns whose frequency significantly deviates from what is expected under a suitable null model of the data, is a key data mining task with application in several areas. We present PROMISE, an algorithm for identifying significant sequential patterns while guaranteeing that the probability that one or more false discoveries are reported in output (i.e., the FamilyWise Error Rate  FWER) is less than a userdefined threshold. PROMISE employs the WestfallYoung method to correct for multiple hypothesis testing, a more powerful method than the commonly used Bonferroni correction. PROMISE crucially hinges on the generation of (random) permuted datasets with features similar to the input dataset, for which we provide two efficient strategies. We also provide a rigorous analysis of one of such strategies, which is based on a properly defined swap operation, proving a rigorous bound on the number of swaps it requires. The results of our experimental evaluation show that PROMISE is an efficient method that allows the discovery of statistically significant sequential patterns from transactional datasets while properly controlling for false discoveries.

A Projectbased PMiner Algorithm in Uncertain Databases Existing works considered the levelwise method to find the high averageutility sequential patterns at different levels, which may cause higher computations and the huge memory requirements. To solve the previous problem of high averageutility sequential pattern mining, a projectbased algorithm called PMiner is presented here to speed up the mining efficiency compared to the previous work. Also, the developed PMiner concerns not only the averageutility on the sequential data but also the data in the uncertain environment (i.e., Internet of Things). Several experiments are then evaluated to show that the proposed algorithm achieves promising performance compared to the levelwise model.