Keynote speaker

 

Sadaaki Miyamoto

Department of Risk Engineering, University of Tsukuba
Ibaraki 305-8573, Japan, miyamoto@risk.tsukuba.ac.jp

Sadaaki Miyamoto is currently a professor of the Department of Risk Engineering, University of Tsukuba, Japan.  He was born in Osaka, Japan, in 1950. He received the B.S.,M.S. and the Dr. Eng. degrees in Applied Mathematics and Physics Engineering from Kyoto University, Japan, in 1973, 1975, and 1978, respectively. Prior to his professorship at the University of Tsukuba, he was an Assistant Professor from 1980 to 1987 and Associate Professor from 1987 to 1990 at the University of Tsukuba. He also was a research scholar at the International Institute for Applied Systems Analysis, Laxenburg, Austria in 1986. He was a Professor with the Faculty of Engineering, the University of Tokushima, where he was working from 1990 to 1994.
His current research interests include methodology for fuzzy systems and uncertainty modeling. In particular he has been working on data clustering algorithms and related classification methods, image indexing and retrieval, theory of generalized bags (alias multisets), rough sets, meta-heuristic optimization, and algorithms for data mining. He is a member of the Society of Instrumentation and Control, Engineers of Japan, Information Processing Society of Japan, the Japan Society of Fuzzy Theory and Systems, and IEEE. He has served a number of international conferences as chairs, co-chairs and committee members. He received excellent paper awards from the Japan Society of Fuzzy Theory and Systems in 1994 and 1999. He has published three books of which two are in English and the other is in Japanese. He also has published 1 edited book and over 250 research papers. In 2007, he became a fellow of the International Fuzzy Systems Association (IFSA).  Moreover he was awarded to be a fellow of the International Society of Management Engineering in 2009.

Recent Developments on Algorithms for Fuzzy Clustering

Abstract— Data clustering has long been studied but recently many more researchers are interested in this area of studies, as data mining techniques are recognized to be an important and useful tool in a variety of sciences and engineering. In this talk we overview recent studies on new methods and algorithms for fuzzy clustering, by focusing upon their theoretical aspects. We first note that fuzzy clustering can be divided into two categories of hierarchical fuzzy clustering and nonhierarchical fuzzy clustering. The former theory was established in 1990, when an old method of the single linkage in agglomerative hierarchical clustering is proved to be equivalent to the transitive closure of a symmetric fuzzy relation. Moreover a key concept for the equivalence is connected components of a fuzzy graph. After noting this, we discuss the best-known method of fuzzy c-means clustering. We note that there are two major objective functions of fuzzy c-means clustering that use a basic alternative optimization procedure with respect to cluster centers and membership matrix. The well-known objective function has been proposed by Dunn and Bezdek, while the other uses an additional term of entropy.  The latter has been proposed by a number of researchers. We show these two methods have different theoretical properties by using fuzzy classifiers naturally derived from the two different types of membership matrices. Although the method of entropy is less-known, we emphasize its importance in methodological sense. Next topic is clustering using kernel functions that are used in support vector machines. How kernel functions are used in fuzzy c-means clustering and related methods is described.  Kernel functions specific to fuzzy clustering are introduced, and application to text analysis is briefly discussed. Cluster validity functions with and without kernel functions are shown and a simulation study comparing the effectiveness of different validity functions using many numerical examples is mentioned. Moreover semi-supervised fuzzy clustering using kernel functions are discussed.

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Van-Nam Huynh

Japan Advanced Institute of Science and Technology (JAIST)

Short bio of Nam:
Van-Nam Huynh holds a Bachelor degree in Mathematics (1990) and a PhD (1999) from the University of Quinhon and the Institute of Information Technology, Vietnam Academy of Science and Technology, respectively.  He was a post-doctoral fellow (2001-2002) awarded by Inoue Foundation for Science at Japan Advanced Institute of Science and Technology (JAIST), where currently he holds an assistant professor position.  His research interests include decision theories, computing and reasoning with words, information fusion, kansei information processing and application, and machine learning. He has published over 70 refereed papers on these subjects and co-edited two Springer volumes, and also been on the program committees of a number of international conferences and workshops. He has been a guest co-editor for International Journal of Approximate Reasoning (Elsevier) and Annals of Operations Research (Springer).

Multiattribute Decision Making under Mixed Evaluations with Uncertainty: A Unified Evidential Reasoning Approach

Abstract: The evaluations for selection and for ranking are the two closely related and common facets of human decision-making activities. Practically, knowledge for decision making is often derived from a range of relevant sources of information and data which are usually heterogeneous and unavoidably associated with uncertainty and imprecision. In addition, many real world decision problems are involved with multiple attributes of both a quantitative and qualitative nature. Conducting such complex multiattribute decision analysis (MADA) is still a significant challenge and has recently attracted considerable attention from the research community.
In this talk, we first present an overview of the recent advances regarding the use of fuzzy set theory, fuzzy integrals, rough set theory, and Dempster-Shafer theory of evidence in MADA. Then we introduce a novel methodology based on Lawry’s interpretation of linguistc variables and the evidential reasoning approach for solving multiattribute decision problems which have mixed evaluations over multiple attributes associated with uncertainty and imprecision. A missile-evaluation problem is used to illustrate the applicability of the new methodology.