Skip to main content

Mining frequent weighted utility itemsets in hierarchical quantitative databases

Authors: 

Ham Nguyen, Tuong Le, Minh Nguyen, Philippe Fournier-Viger, Vincent S. Tseng, Bay Vo

Source title: 
Knowledge-Based Systems, 237: 107709, 2022 (ISI)
Academic year of acceptance: 
2021-2022
Abstract: 

Mining frequent itemsets in traditional databases and quantitative databases (QDBs) has drawn many researchers’ interest. Although many studies have been conducted on this topic, a major limitation of these studies is that they ignore the relationships between items. However, in real-life datasets, items are often related to each other through a generalization/specialization relationship. To consider the relationships and discover a more generalized form of patterns, this study proposes a new concept of mining frequent weighted utility itemsets in hierarchical quantitative databases (HQDBs). In this kind of databases, items are organized in a hierarchy. Using the extended dynamic bit vector structure with large integer elements, two efficient algorithms named MINE_FWUIS and FAST_MINE_FWUIS are developed. The empirical evaluations in terms of processing time between MINE_FWUIS and FAST_MINE_FWUIS are conducted. The experimental results indicate that FAST_MINE_FWUIS is recommended for mining frequent weighted utility itemsets in hierarchical QDBs.