A Survey of Utility-Oriented Pattern Mining

Wensheng Gan, Jerry Chun Wei Lin*, Philippe Fournier-Viger, Han Chieh Chao, Vincent Shin-Mu Tseng, Philip S. Yu

*此作品的通信作者

研究成果: Article同行評審

181 引文 斯高帕斯(Scopus)

摘要

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, examples, and comparisons with related concepts. A taxonomy of the most common and state-of-the-art approaches for mining different kinds of high-utility patterns is presented in detail, including Apriori-based, tree-based, projection-based, vertical-/horizontal-data-format-based, and other hybrid approaches. A comprehensive review of advanced topics of existing high-utility pattern mining techniques is offered, with a discussion of their pros and cons. Finally, we present several well-known open-source software packages for UPM. We conclude our survey with a discussion on open and practical challenges in this field.

原文English
文章編號8845637
頁(從 - 到)1306-1327
頁數22
期刊IEEE Transactions on Knowledge and Data Engineering
33
發行號4
DOIs
出版狀態Published - 1 4月 2021

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