Incrementally updating the high average-utility patterns with pre-large concept

High-utility itemset mining (HUIM) is considered as an emerging approach to detect the high-utility patterns from databases. Most existing algorithms of HUIM only consider the itemset utility regardless of the length. This limitation raises the utility as a result of a growing itemset size. High average-utility itemset mining (HAUIM) considers the size of the itemset, thus providing a more balanced scale to measure the average-utility for decision-making. Several algorithms were presented to efficiently mine the set of high average-utility itemsets (HAUIs) but most of them focus on handling static databases. In the past, a fast-updated (FUP)-based algorithm was developed to efficiently handle the incremental problem but it still has to re-scan the database when the itemset in the original database is small but there is a high average-utility upper-bound itemset (HAUUBI) in the newly inserted transactions. In this paper, an efficient framework called PRE-HAUIMI for transaction insertion in dynamic databases is developed, which relies on the average-utility-list (AUL) structures. Moreover, we apply the pre-large concept on HAUIM. A pre-large concept is used to speed up the mining performance, which can ensure that if the total utility in the newly inserted transaction is within the safety bound, the small itemsets in the original database could not be the large ones after the database is updated. This, in turn, reduces the recurring database scans and obtains the correct HAUIs. Experiments demonstrate that the PRE-HAUIMI outperforms the state-of-the-art batch mode HAUI-Miner, and the state-of-the-art incremental IHAUPM and FUP-based algorithms in terms of runtime, memory, number of assessed patterns and scalability.publishedVersio

Efficient algorithms for mining clickstream patterns using pseudo-IDLists

Sequential pattern mining is an important task in data mining. Its subproblem, clickstream pattern mining, is starting to attract more research due to the growth of the Internet and the need to analyze online customer behaviors. To date, only few works are dedicately proposed for the problem of mining clickstream patterns. Although one approach is to use the general algorithms for sequential pattern mining, those algorithms’ performance may suffer and the resources needed are more than would be necessary with a dedicated method for mining clickstreams. In this paper, we present pseudo-IDList, a novel data structure that is more suitable for clickstream pattern mining. Based on this structure, a vertical format algorithm named CUP (Clickstream pattern mining Using Pseudo-IDList) is proposed. Furthermore, we propose a pruning heuristic named DUB (Dynamic intersection Upper Bound) to improve our proposed algorithm. Four real-life clickstream databases are used for the experiments and the results show that our proposed methods are effective and efficient regarding runtimes and memory consumption. © 2020 Elsevier B.V.Vietnam National Foundation for Science and Technology Development (NAFOSTED)National Foundation for Science & Technology Development (NAFOSTED) [02/2019/TN