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2017-12-07 19:20:02
2017-12-07
課題組成員受邀撰稿，在綜述類SCI 源刊和開源期刊發(fā)表長篇綜述論文4篇
在2016-2017年度,，林?，|教授的課題組成員受主編邀請撰稿，先后歷時了6-12個月的pre-peer view, 在JCR 2區(qū)綜述類SCI 源刊《Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery》（SCI, JCR 2區(qū), IF:2.111）和數(shù)據(jù)科學(xué)與模式識別的開源新期刊《Ubiquitous International Data Science and Pattern Recognition》（Open access, ISSN 2520-4165）上發(fā)表高水平長篇綜述論文4篇,。研究主題涵蓋了數(shù)據(jù)挖掘領(lǐng)域的幾個經(jīng)典研究與應(yīng)用子領(lǐng)域,，具體包括了：頻繁模式挖掘和關(guān)聯(lián)規(guī)則挖掘（frequent itemset mining & association rule mining）、序列模式/規(guī)則挖掘（sequential pattern/rule mining）,、分布式數(shù)據(jù)挖掘（distributed data mining）,、高效用模式的增量挖掘（incremental high utility pattern mining）等。哈爾濱工業(yè)大學(xué)（深圳）均為論文的第一作者單位,。
《Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery》（SCI, JCR 2區(qū),，IF:2.111），WIRES DMKD是數(shù)據(jù)挖掘領(lǐng)域知名的綜述性評論期刊,，其2016年影響因子為IF:2.111, 每年僅發(fā)表二十余篇學(xué)術(shù)論文，一般由在各領(lǐng)域有不錯建樹的研究學(xué)者受邀撰稿,，旨在對當(dāng)今數(shù)據(jù)挖掘和知識發(fā)現(xiàn)研究的經(jīng)典問題和熱點問題做歷史總結(jié),、原理闡述、現(xiàn)狀分析和趨向預(yù)測,。先由主編邀請撰稿,，然后還要經(jīng)過6-12個月的pre-peer view同行評審?！?a target="_self">Ubiquitous International Data Science and Pattern Recognition》,，DSPR是數(shù)據(jù)科學(xué)與模式識別領(lǐng)域的開源新期刊，收錄人工智能,、機器學(xué)習(xí),、數(shù)據(jù)挖掘、模式識別等各個領(lǐng)域的綜述類文章或原創(chuàng)性文章,，其ISSN 為2520-4165,。
本課題組于2017年度應(yīng)邀撰稿的4篇長篇綜述類論文的具體信息如下所述。歡迎同行學(xué)者們的下載,、閱讀,、批評指正,、引用拓展。特別地,，熱烈歡迎對上述研究領(lǐng)域感興趣或有致力于擴展研究的同行與我們交流,、探討、合作論文,。
綜述論文1
Title: A survey of itemset mining
文章鏈接：http://onlinelibrary.wiley.com/doi/10.1002/widm.1207/full
Authors: Philippe Fournier-Viger 1*, Jerry Chun-Wei Lin 2, Bay Vo 3,4, Tin Truong Chi 5, Ji Zhang 6, and Hoai Bac Le 7
Journal: 《Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery》（SCI, JCR 2區(qū),，IF:2.111）。
Abstract:
Itemset mining is an important subfield of data mining, which consists of discovering interesting and useful patterns in transaction databases. The traditional task of frequent itemset mining is to discover groups of items (itemsets) that appear frequently together in transactions made by customers. Although itemset mining was designed for market basket analysis, it can be viewed more generally as the task of discovering groups of attribute values frequently cooccurring in databases. Because of its numerous applications in domains such as bioinformatics, text mining, product recommendation, e-learning, and web click stream analysis, itemset mining has become a popular research area. This study provides an up-to-date survey that can serve both as an introduction and as a guide to recent advances and opportunities in the field. The problem of frequent itemset mining and its applications are described. Moreover, main approaches and strategies to solve itemset mining problems are presented, as well as their characteristics are provided. Limitations of traditional frequent itemset mining approaches are also highlighted, and extensions of the task of itemset mining are presented such as high-utility itemset mining, rare itemset mining, fuzzy itemset mining, and uncertain itemset mining. This study also discusses research opportunities and the relationship to other popular pattern mining problems, such as sequential pattern mining, episode mining, subgraph mining, and association rule mining. Main open-source libraries of itemset mining implementations are also briefly presented.
綜述論文2
Title: A Survey of Sequential Pattern Mining
文章鏈接：http://www.ikelab.net/dspr-pdf/vol1-1/dspr-paper5.pdf
Authors: Philippe Fournier-Viger 1*, Jerry Chun-Wei Lin 2, Rage Uday Kiran 3, Yun Sing Koh 4, and Rincy Thomas 5
Journal: 《Ubiquitous International Data Science and Pattern Recognition》（Open access, ISSN 2520-4165）
Abstract:
Discovering unexpected and useful patterns in databases is a fundamental data mining task. In recent years, a trend in data mining has been to design algorithms for discovering patterns in sequential data. One of the most popular data mining tasks on sequences is sequential pattern mining. It consists of discovering interesting subsequences in a set of sequences, where the interestingness of a subsequence can be measured in terms of various criteria such as its occurrence frequency, length, and profit. Sequential pattern mining has many real-life applications since data is encoded as sequences in many fields such as bioinformatics, e-learning, market basket analysis, text analysis, and webpage click-stream analysis. This paper surveys recent studies on sequential pattern mining and its applications. The goal is to provide both an introduction to sequential pattern mining, and a survey of recent advances and research opportunities. The paper is divided into four main parts. First, the task of sequential pattern mining is defined and its applications are reviewed. Key concepts and terminology are introduced. Moreover, main approaches and strategies to solve sequential pattern mining problems are presented. Limitations of traditional sequential pattern mining approaches are also highlighted, and popular variations of the task of sequential pattern mining are presented. The paper also presents research opportunities and the relationship to other popular pattern mining problems. Lastly, the paper also discusses open-source implementations of sequential pattern mining algorithms.
Keywords: Sequential pattern mining, Sequences, Frequent pattern mining, Itemset mining, Data Mining
綜述論文3
Title: Data mining in distributed environment: a survey
文章鏈接：http://onlinelibrary.wiley.com/doi/10.1002/widm.1216/full
Authors: Wensheng Gan 1, Jerry Chun-Wei Lin 1*, Han-Chieh Chao 1,2, and Justin Zhan 3
Journal: 《Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery》（SCI, JCR 2區(qū),，IF:2.111）
Abstract:
Due to the rapid growth of resource sharing, distributed systems are developed, which can be used to utilize the computations. Data mining (DM) provides powerful techniques for finding meaningful and useful information from a very large amount of data, and has a wide range of real-world applications. However, traditional DM algorithms assume that the data is centrally collected, memory-resident, and static. It is challenging to manage the large-scale data and process them with very limited resources. For example, large amounts of data are quickly produced and stored at multiple locations. It becomes increasingly expensive to centralize them in a single place. Moreover, traditional DM algorithms generally have some problems and challenges, such as memory limits, low processing ability, and inadequate hard disk, and so on. To solve the above problems, DM on distributed computing environment [also called distributed data mining (DDM)] has been emerging as a valuable alternative in many applications. In this study, a survey of state-of-the-art DDM techniques is provided, including distributed frequent itemset mining, distributed frequent sequence mining, distributed frequent graph mining, distributed clustering, and privacy preserving of distributed data mining. We finally summarize the opportunities of data mining tasks in distributed environment
綜述論文4
Title: A Survey of Incremental High-Utility Itemset Mining
Authors: Wensheng Gan 1, Jerry Chun-Wei Lin 1*, Philippe Fournier-Viger 2, Han-Chieh Chao 1,3, Tzung-Pei Hong 4, and Hamido Fujita 5
Journal: 《Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery》（SCI, JCR 2區(qū),，IF:2.111）
Abstract:
Traditional association rule mining has been widely studied. But it is unsuitable for real-world applications where factors such as unit profits of items and purchase quantities must be considered. High-utility itemset mining (HUIM) is designed to find highly profitable patterns by considering both the purchase quantities and unit profits of items. However, most high-utility itemset mining algorithms are designed to be applied on static databases. But in real-world applications such as market basket analysis and business decision making, databases are often dynamically updated by inserting new data such as customer transactions. Several researchers have proposed algorithms to discover high-utility itemsets in dynamically updated databases. Unlike batch algorithms, which always process a database from scratch, incremental HUIM algorithms incrementally update and output HUIs, thus reducing the cost of discovering HUIs. This paper provides an up-to-date survey of the state-of-the-art incremental high-utility itemset mining algorithms, including Apriori-based, tree-based, and utility-list-based approaches. The paper also identifies several important issues and research challenges for incremental high-utility itemset mining.
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