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Granular Clustering: Augmenting Principles, Identifying New Directions and Forming Application-Oriented Implications
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讲座题目:Granular Clustering: Augmenting Principles, Identifying New Directions and Forming Application-Oriented Implications

讲座人: Witold Pedrycz 加拿大皇家科学院院士

讲座时间:4:00-5:30

讲座日期:2018-3-12

讲座地点:长安校区 图书馆西附楼学术报告厅

主办单位:计算机科学学院、图书馆

讲座内容简介:

Clustering has been for decades a focal point of studies quite often researched in relation with modeling, pattern classification, and data analysis. With the advent of data analytics bringing a suite of new problems, clustering has been subjected to 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.

 

The term granular clustering can be sought from the two general points of view; in this talk those perspectives are carefully analyzed along with a formulation of far reaching ramifications. The first general view is concerned with the formation of information granules completed on a basis of predominantly numeric (non-granular) data. The alternative view stresses the clustering of granular data themselves. The hybrid of these views are also investigated.

 

In the setting of data analytics, there are several well-articulated and emerging challenges. 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) 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.

 

Clustering techniques are commonly concerned with the formation of direction-free  (relational) constructs such as those being used in association (linkage) analysis. The accommodation of the aspect of directionality (required to cope with in various modeling tasks) entails another wave of pursuits that are referred to as direction-sensitive clustering.

 

Distributed data environment when various sources of data are to be analyzed en block calls for clustering realized in the space of information granules. We discuss a concept of double level clustering where the concept of tensor distance is involved in order to capture the interrelationships between granular data encountered in the distributed environment.

 

讲座人简介:

Witold Pedrycz is Professor and Canada Research Chair (CRC) in Computational Intelligence in the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada. He is also with the Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland. He is a foreign member of the Polish Academy of Sciences, and a Fellow of the Royal Society of Canada, IEEE, and IFSA. He is a recipient of numerous awards including a prestigious Norbert Wiener award from the IEEE Systems, Man, and Cybernetics Society, IEEE Canada Computer Engineering Medal, Cajastur Prize for Soft Computing, Killam Prize, and a Fuzzy Pioneer Award from the IEEE Computational Intelligence Society.

 

His main research directions involve Computational Intelligence, fuzzy modeling and Granular Computing, knowledge discovery and data mining, fuzzy control, pattern recognition, knowledge-based neural networks, relational computing, and Software Engineering. He is an author of 15 research monographs covering various aspects of Computational Intelligence, data mining, and Software Engineering.

 

Dr. Pedrycz is intensively involved in editorial activities. He is an Editor-in-Chief of Information Sciences and Editor-in-Chief of WIREs Data Mining and Knowledge Discovery (Wiley).  He currently serves as an Associate Editor of IEEE Transactions on Fuzzy Systems and is a member of a number of editorial boards of other international journals.