Mining high utility pattern with negative items in dynamic databases. Issue 8 (29th December 2021)

• Record Type: Journal Article
• Authors: Han, Meng; Zhang, Ni; Wang, Le; Li, Xiaojuan; Cheng, Haodong
• Is Part Of: International journal of intelligent systems. Volume 37:Issue 8(2022)
• Page Start: 5325
• Page End: 5353
• Publication Date: 2021-12-29
• Subjects: high utility pattern mining -- index -- negative utility -- sliding window -- utility list; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.22794
• Languages: English
• ISSNs: 0884-8173

Abstract

Abstract: High utility pattern mining with negative items (HUPMN) has more practical applications because it can process data with negative utility values. But the existing HUPMN algorithms assume that the database is static and it would be very expensive to use these algorithms directly to deal with dynamic databases. To cope with this challenge, an high utility pattern algorithm for mining negative items from an incremental database is proposed for the first time, and an incremental index list structure is designed, which uses index values to quickly access and update the information stored in the list. In addition, A memory reuse strategy is also applied to reduce memory usage. Finally, a HUPMN algorithm based on sliding window was proposed, which can quickly update item information when the window was sliding. Extensive experimental evaluations have been carried out on a variety of data sets, and the results show that the proposed algorithm exhibits excellent performance in terms of running time and memory usage.