学术活动

人工智能与数据挖掘学术报告

活动品牌 大连理工大学-学术活动
主 讲 人 刘传人助理教授,刘淇副教授
活动地点 黑楼二楼报告厅
开始时间 2017-09-15 14:00
结束时间 2017-09-15 16:00

活动简介:

报告一:Temporal Correlation in Sequential Pattern Analysis

摘要:Sequential pattern analysis aims at finding statistically relevant temporal structures where the values are delivered in sequences. This is a fundamental problem in data mining with diversified applications in many science and business fields. Given the overwhelming scale and the dynamic nature of the sequential data, new visions and strategies for sequential pattern analysis are required to derive competitive advantages and unlock the power of the big data. To this end, in this talk, we present novel approaches for sequential pattern analysis using temporal correlation. Particularly, we will focus on the “temporal skeletonization”, our approach to identifying the meaningful granularity for sequential pattern mining. We first show that a large number of symbols in a sequence can “dilute” useful patterns which themselves exist at a different level of granularity. This is so-called “curse of cardinality”, which can impose significant challenges to the design of sequential analysis methods. To address this challenge, our key idea is to summarize the temporal correlations in an undirected graph, and use the “skeleton” of the graph as a higher granularity on which hidden temporal patterns are more likely to be identified. In the meantime, the embedding topology of the graph allows us to translate the rich temporal content into a metric space. This opens up new possibilities to explore, quantify, and visualize sequential data.

报告人简介:

Chuanren Liu is currently an Assistant Professor in the Decision Sciences and MIS Department at Drexel University. He received the Ph.D. in Management (Information Technology) from Rutgers, the State University of New Jersey, USA, the M.S. degree in Mathematics from the Beijing University of Aeronautics and Astronautics (BUAA), and the B.S. degree in Mathematics from the University of Science and Technology of China (USTC). His research interests include data mining and knowledge discovery, and their applications in business analytics. He has published papers in refereed journals and conference proceedings, such as European Journal of Operational Research, Annals of Operations Research, IEEE Transactions on Data and Knowledge Engineering, IEEE Transactions on Cybernetics, Knowledge and Information Systems and KDD, ICDM, SDM, AAAI, UbiComp, etc.

报告二:领域知识结合的用户行为数据挖掘方法及应用

摘要:

在当今大数据时代,社会个体在诸多领域的行为轨迹能够被大量的记录和保存,如消费行为数据、社交行为数据、教育相关的行为数据等,这些社会化行为数据是学习用户行为模式和了解社会运行规律的重要信息来源,有着迫切的研究价值和应用需求。然而,应用场景的多样性、领域特征的复杂性等,使得传统的数据挖掘结果难以有效满足不同场景的实际需求,在已有方法基础上,必须发展新的理论框架和处理算法。为此,本报告将以各领域记录的用户行为数据为研究对象,以用户理解为核心,从数据挖掘方法与领域知识联合建模的角度,介绍我们近期在用户行为数据挖掘方法及应用方面的一些探索工作。

报告人简介:

刘淇,党员,博士,中国科学技术大学计算机学院副教授,中国计算机学会(CCF)大数据专家委员会委员、中国人工智能学会机器学习专委会委员。主要研究数据挖掘与知识发现、机器学习方法及其应用,着重于针对用户行为数据(如消费数据、社交数据、教育数据等)的建模和应用研究。在重要国际学术会议和期刊共发表论文60余篇,2011年获得数据挖掘领域顶级国际会议之一IEEE ICDM的最佳研究论文奖,还获中科院院长特别奖、KSEM 2013最佳论文奖以及SDM 2015 最佳论文候选奖、中科院优博等重要学术奖励,入选中科院青年创新促进会。主持了多项国家、省部级以及与知名公司(如微软、腾讯、科大讯飞)的合作研究项目。担任了CCF大数据学术会议(BigData)2015-2017的宣传主席、是包括IJCAI、KDD、WWW、AAAI、ICDM、CIKM等国际会议的程序委员会委员以及国际期刊TKDE、TKDD、TC、TSMC-C、TIST等的审稿人、是FCS青年AE。