High utility itemsets mining from transactional databases: a survey

High utility itemsets mining from transactional databases: a survey

연구 분야: Databases

학회: Applied Intelligence

Mining high utility itemsets are the basic task in the area of frequent itemset mining (FIM) that has various applications in diverse domains, including market basket analysis, web mining, cross-marketing, and e-commerce. In recent years, many efficient high utility itemsets mining (HUIM) algorithms are proposed to discover the high utility itemsets (HUIs). This survey presents a comprehensive summary of the current state-of-the-art HUIM approaches for transactional databases. This paper categorises the state-of-the-art approaches as level-wise, tree-based, utility-list-based, projection-based and miscellaneous. It provides the pros and cons of each category of mining approaches in detail. A taxonomy of the HUIM for transactional databases is presented. The survey also summarises and discusses approaches for other types of databases, including on-shelf, dynamic and uncertain. The paper explores the applications of HUIM in diverse domains and discusses the challenges and limitations of the approach. It presents an overview of 16 real-world which are utilized by various state-of-the-art HUIM approaches for transactional databases. Overall, this survey provides a valuable resource for researchers in the field of HUIM and offers insights into future directions for research and development in this area.