效用挖掘技术长篇综述在数据挖掘顶级期刊IEEE TKDE在线发表

      日前，哈工大深圳的甘文生博士撰写的关于效用挖掘Utility Mining的长篇综述，先后历时18个月的peer-review, 在数据库与数据挖掘等领域的顶级期刊IEEE Transactions on Knowledge and Data Engineering(SCI, IF:3.438, CCF A）在线发表，DOI:10.1109/TKDE.2019.2942594 , 哈尔滨工业大学(深圳)为论文的第一作者单位。本文的完成人包括: 哈工大深圳的甘文生、西挪威应用科技大学的林浚玮教授 (原哈工大深圳副教授, 已于2018年6月离职)、哈工大深圳的Philippe Fournier-Viger教授，台湾东华大学的赵涵捷教授、美国伊利诺伊大学芝加哥分校的Philip S. Yu教授等人。该长篇综述针对基于效用驱动的模式挖掘技术(Utility-oriented Pattern Mining)的研究背景与意义、应用案例、经典研究问题、算法分类与原理、发展研究现状做出了详细的回顾、原理阐述、现状分析和总结。该论文是IEEE TKDE自1989年创刊以来发表的以哈尔滨工业大学(深圳)为第一作者单位的第一篇长篇综述。

IEEE Transactions on Knowledge and Data Engineering (SCI, IF:3.438, CCF A), IEEE TKDE是数据库、数据挖掘等领域的最具影响力的国际期刊，CCF A类期刊。中国计算机学会将IEEE TKDE定位为数据库/数据挖掘/内容检索领域4个A类国际期刊之一。“A类指国际上极少数的顶级刊物和会议，鼓励我国学者去突破”。该学术期刊每年出版12期，共收录200篇文章左右。

论文题目: A survey of utility-oriented pattern mining

文章链接：https://ieeexplore.ieee.org/document/8845637

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

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, 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.