A Survey of Utility-Oriented Pattern Mining
Wensheng Gan, Jerry Chun-Wei Lin, Philippe Fournier-Viger, Han-Chieh, Chao, Vincent S. Tseng, and Philip S. Yu

TL;DR
This survey comprehensively reviews utility-oriented pattern mining (UPM), covering concepts, methods, advanced topics, software tools, and challenges, highlighting its importance in various real-world applications.
Contribution
It provides a structured overview of state-of-the-art UPM techniques, including taxonomy, comparisons, and discussions on open challenges and software tools.
Findings
Taxonomy of UPM approaches including Apriori, tree-based, and hybrid methods.
Analysis of pros and cons of different UPM techniques.
Identification of open challenges and future research directions.
Abstract
The main purpose of data mining and analytics is to find novel, potentially useful patterns that can be utilized in real-world applications to derive beneficial knowledge. For identifying and evaluating the usefulness of different kinds of patterns, many techniques and constraints have been proposed, such as support, confidence, sequence order, and utility parameters (e.g., weight, price, profit, quantity, satisfaction, etc.). In recent years, there has been an increasing demand for utility-oriented pattern mining (UPM, or called utility mining). UPM is a vital task, with numerous high-impact applications, including cross-marketing, e-commerce, finance, medical, and biomedical applications. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods of UPM. First, we introduce an in-depth understanding of UPM, including concepts,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
