A General Method for mining high-Utility itemsets with correlated measures. Issue 4 (2nd October 2021)

• Record Type: Journal Article
• Authors: Hung, Nguyen Manh; NT, Tung; Vo, Bay
• Is Part Of: Journal of information and telecommunication. Volume 5:Issue 4(2021)
• Page Start: 536
• Page End: 549
• Publication Date: 2021-10-02
• Subjects: high-utility itemset -- high-correlated itemset -- general method; Telecommunication -- Periodicals; Information technology -- Periodicals; 621.382
• DOI: 10.1080/24751839.2021.1937465
• Languages: English
• ISSNs: 2475-1839

Abstract

ABSTRACT: Discovering high-utility itemsets from a transaction database is one of the important tasks in High-Utility Itemset Mining (HUIM). The discovered high-utility itemsets (HUIs) must meet a user-defined given minimum utility threshold. Several methods have been proposed to solve the problem efficiently. However, they focused on exploring and discovering the set of HUIs. This research proposes a more generalized approach to mine HUIs using any user-specified correlated measure, named the General Method for Correlated High-utility itemset Mining (GMCHM). This proposed approach has the ability to discover HUIs that are highly correlated, based on the all_confidence and bond measures (and 38 other correlated measures). Evaluations were carried out on the standard datasets for HUIM, such as Accidents, BMS_utility and Connect. The results proved the high effectiveness of GMCHM in terms of running time, memory usage and the number of scanned candidates.