In the era of big data, targeted querying of interesting itemsets having concise expressions is promising for improving the efficiency and capability of data mining applications. As the full set of high-utility itemsets is no longer explored, mining becomes more efficient. Nonetheless, identifying whether the current itemset includes the target pattern and is closed remains a challenge for enhancing data analysis efficiency. At present, no single-phase algorithm reliably identifies the targeted closed high-utility itemsets. In this article, we propose an algorithm called TQCUI, Targeted Querying of Closed high-Utility Itemsets containing the target patterns in a transactional database. The algorithm employs a compact attribute-utility-list structure for maintaining the utility and attribute information of the itemsets. Additionally, TQCUI utilizes several efficient pruning strategies to filter out unpromising itemsets, substantially reducing the search space. Moreover, to quickly prune non-closed itemsets, TQCUI introduces forward-extension and backward-extension checking schemes for the closure checking of itemsets. Extensive experimentation on both real and synthetic datasets demonstrates the TQCUI algorithm has good performance in terms of runtime, memory consumption, and scalability.

Negative sequential pattern (NSP) mining is crucial and sometimes carries more enlightening information than positive sequential pattern (PSP) mining in data mining. Owing to its computational complexity and exponential search space, the task of discovering NSPs is often much more difficult and challenging than that for PSPs. To date, a few NSP mining algorithms have been proposed. However, most algorithms only consider a single support, thus can not present good results in many special real-world applications. To solve this problem and achieve better efficiency on a long sequence database or a large-scale database, we propose a novel algorithm called Negative Sequential Patterns with Individual Support (NSPIS) in this paper. The projection mechanism is adopted to NSPIS, which allows greatly reduce the search space and simultaneously improve the efficiency. Finally, detailed results of the experiments show that NSPIS can achieve better performance and it uses less memory on large datasets compared to the state-of-the-art algorithm.