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转发:名师讲堂: From Data to Information Granules via Granular Clustering

来源:人事人才办公室    点击:938    发布时间:2018-07-13 11:27

    人力源部教师发展中心“名师讲堂”第88期活安排如下,迎广大生参加

    一、主题: From Data to Information Granules via Granular  Clustering

    二、时间: 2018年7月16日(周一)15:00

    三、地点: 清水河校区图书馆二楼光影厅

    四、主讲人: Witold Pedrycz教授(IEEE Fellow、CANADA RESEARCH CHAIR(TIER I)、加拿大皇家科学院院士、波兰科学院院士

     五、主持人:机械与电气工程学院 李迅波 教授

     六、内容简介:

     For decades, clustering has been a focal point of studies quite often researched in relationship with modeling, pattern classification, and data analysis. With the advent of data analytics bringing a suite of new challenges and problems, clustering has undergone a visible paradigm shift. Granular clustering, the term being recently used, has emphasized the role of clustering regarded as a sound vehicle to construct information granules – entities aimed at the building abstract yet flexible and adjustable views at data, facilitating processing of masses of data and subsequently constructing interpretable models.

     Considering objective function-based clustering, these techniques return a small number of numeric representatives (prototypes) of big data. This triggers a question as to the representation capabilities of the prototypes. A certain line of research is to augment the numeric prototypes produced by their granular generalizations (viz. granular prototypes) and optimize their abilities to capture the essence of the data. We discuss a direction of research aimed at building optimal granular prototypes and their characterization. It is shown that some clustering techniques exhibiting a great deal of flexibility (such as e.g., DBSCAN or hierarchical clustering) still require a concise characterization of the comprehensive results coming in the form of granular prototypes. An impact on ensuing modeling (viz. modeling exploiting granular data) is discussed.

     While the results of clustering algorithms are commonly conveyed through numeric constructs (say, prototypes and partition matrices, etc.), we discuss here an attractive alternative of symbolic (qualitative) characterization of information granules (clusters), which supports higher levels of interpretability and offers insights into aspects of stability of structural findings.

    七、主讲人简介:

    Dr. Witold Pedrycz是IEEE Fellow,加拿大阿尔伯塔大学电子与计算机工程系教授、博导,加拿大计算智能首席(Canadian Research Chair Tier I)科学家,加拿大皇家科学院院士,波兰科学院外籍院士,主要研究方向为计算智能、数据挖掘、模糊控制等。他是IFSA Fellow(2005), IEEE Fellow(1999),International Society of Management Engineers Life Fellow(2011)。因其在模糊系统方面做出的卓越创新,曾担任国际模糊系统联合会(IFSA)和北美模糊系统协会(NAFIPS)主席,2018年被授予电子科技大学名誉教授。

他发表SCI检索期刊论文630+篇及17本研究专著,其中IEEE Transactions论文100+篇,被SCI他引46000余次,Google Scholar显示H指数为98。近五年发表研究专著3本、期刊论文182篇、主编论文集6卷。因其在计算机科学与工程领域做出的巨大贡献,于2007年获得 IEEE Norbert Wiener Award,该奖是IEEE SMC 学会颁发的最高技术成就奖;于2009年获Soft Computing领域国际最高奖Cajastur Prize,于2013年获得加拿大的Killam Prize以及IEEE加拿大计算机工程勋章等,在国际学术界具有极高影响力。

 八、主办单位:人力资源部教师发展中心

   承办单位:机械与电气工程学院

                                                                                                                  人力资源部教师发展中心

                                                     2018年7月13日