Towards efficient discovery of target High Utility Itemsets

Vincent Nofong, Priscilla Okai, Hamidu Abdel-Fatao, Selasi Kwashie, Michael Bewong, John Wondoh

Research output: Book chapter/Published conference paperConference paperpeer-review


Finding High Utility Itemsets (HUIs) in databases is crucial for identifying items that are of high importance (like profit) for decision-making. However, current High Utility ItemsetMining (HUIM) algorithms often ignore the interest or target of users in favor of effectively identifying categories of HUIs using various measures and constraints. As a result, these techniques usually return a set of HUIs that are either too large or small to employ in decision making. Nevertheless, it is apparent that users are often interested in a select group of HUIs which may be among the set of HUIs reported using existing techniques. While some recent and early works have offered methods for discovering user-targeted HUIs, these methods are neither memory- nor time-efficient as they depend on post-processing or pattern matching. Additionally, throughout the discovery process, these techniques are required to scan the database twice. To address these issues,this paper proposes an efficient Target High Utility Itemset Miner (TarHUIM). In contrast to current methods, TarHUIM uses the users’ target list and a single database scan to significantly reduce the search space and amount of time needed to find the user-targeted HUIs. Extensive experimental analysis show that TarHUIM is efficient and effective in discovering the set of targeted HUIs.
Original languageEnglish
Title of host publicationProceedings, 22nd IEEE International Conference on Data Mining Workshops
Subtitle of host publicationICDMW 2022
EditorsK. Selçuk Candan, Thang N. Dinh, My T. Thai, Takashi Washio
Number of pages10
ISBN (Electronic)9798350346091
ISBN (Print)9798350346107
Publication statusPublished - 2022
Event22nd IEEE International Conference on Data Mining: IEEE ICDM 2022 - Hilton Orlando, Orlando, United States
Duration: 28 Nov 202201 Dec 2022 (Proceedings) (Program)

Publication series

ISSN (Print)2375-9232
ISSN (Electronic)2375-9259


Conference22nd IEEE International Conference on Data Mining
Country/TerritoryUnited States
OtherThe IEEE International Conference on Data Mining (ICDM) has established itself as the world’s premier research conference in data mining. It provides an international forum for presentation of original research results, as well as exchange and dissemination of innovative and practical development experiences. The conference covers all aspects of data mining, including algorithms, software, systems, and applications. ICDM draws researchers, application developers, and practitioners from a wide range of data mining related areas such as big data, deep learning, pattern recognition, statistical and machine learning,databases, data warehousing, data visualization, knowledge-based systems, and high-performance computing. By promoting novel, high-quality research findings, and innovative solutions to challenging data mining problems, the conference seeks to advance the state-of-the-art in data mining.
Internet address


Dive into the research topics of 'Towards efficient discovery of target High Utility Itemsets'. Together they form a unique fingerprint.

Cite this