期刊
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
卷 33, 期 4, 页码 1306-1327出版社
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2019.2942594
关键词
Data mining; Itemsets; Task analysis; Biomedical measurement; Gallium nitride; Taxonomy; Data science; economics; utility theory; utility mining; high-utility pattern; application
类别
资金
- Shenzhen Technical Project [KQJSCX 20170726103424709, JCYJ 20170307151733005]
- China Scholarship Council Program
The main purpose of data mining and analytics is to discover novel and potentially useful patterns. Utility-oriented pattern mining (UPM) has become increasingly important in various applications. This survey provides an overview of state-of-the-art methods for UPM, including techniques, applications, and challenges in the field.
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.
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