效用挖掘技術(shù)長(zhǎng)篇綜述在數(shù)據(jù)挖掘頂級(jí)期刊IEEE TKDE在線發(fā)表

      日前，哈工大深圳的甘文生博士撰寫(xiě)的關(guān)于效用挖掘Utility Mining的長(zhǎng)篇綜述，先后歷時(shí)18個(gè)月的peer-review, 在數(shù)據(jù)庫(kù)與數(shù)據(jù)挖掘等領(lǐng)域的頂級(jí)期刊IEEE Transactions on Knowledge and Data Engineering(SCI, IF:3.438, CCF A）在線發(fā)表，DOI:10.1109/TKDE.2019.2942594 , 哈爾濱工業(yè)大學(xué)(深圳)為論文的第一作者單位。本文的完成人包括: 哈工大深圳的甘文生、西挪威應(yīng)用科技大學(xué)的林?，|教授 (原哈工大深圳副教授, 已于2018年6月離職)、哈工大深圳的Philippe Fournier-Viger教授，臺(tái)灣東華大學(xué)的趙涵捷教授、美國(guó)伊利諾伊大學(xué)芝加哥分校的Philip S. Yu教授等人。該長(zhǎng)篇綜述針對(duì)基于效用驅(qū)動(dòng)的模式挖掘技術(shù)(Utility-oriented Pattern Mining)的研究背景與意義、應(yīng)用案例、經(jīng)典研究問(wèn)題、算法分類與原理、發(fā)展研究現(xiàn)狀做出了詳細(xì)的回顧、原理闡述、現(xiàn)狀分析和總結(jié)。該論文是IEEE TKDE自1989年創(chuàng)刊以來(lái)發(fā)表的以哈爾濱工業(yè)大學(xué)(深圳)為第一作者單位的第一篇長(zhǎng)篇綜述。

IEEE Transactions on Knowledge and Data Engineering (SCI, IF:3.438, CCF A), IEEE TKDE是數(shù)據(jù)庫(kù)、數(shù)據(jù)挖掘等領(lǐng)域的最具影響力的國(guó)際期刊，CCF A類期刊。中國(guó)計(jì)算機(jī)學(xué)會(huì)將IEEE TKDE定位為數(shù)據(jù)庫(kù)/數(shù)據(jù)挖掘/內(nèi)容檢索領(lǐng)域4個(gè)A類國(guó)際期刊之一。“A類指國(guó)際上極少數(shù)的頂級(jí)刊物和會(huì)議，鼓勵(lì)我國(guó)學(xué)者去突破”。該學(xué)術(shù)期刊每年出版12期，共收錄200篇文章左右。

論文題目: A survey of utility-oriented pattern mining

文章鏈接：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.