Details

Autor(en) / Beteiligte

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

Titel

Mining frequent weighted utility itemsets in hierarchical quantitative databases

Ist Teil von

• Knowledge-based systems, 2022-02, Vol.237, p.107709, Article 107709

Ort / Verlag

Amsterdam: Elsevier B.V

Erscheinungsjahr

2022

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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.