From Data to Concepts: Augmented Principles, Symbolic-Granular Descriptions, and Quality Analysis
Concepts constitute a concise manifestation of key features of data. As being built at a higher level of abstraction than the collected data themselves, they capture the essence of the data and usually emerge in the form of information granules.
In this talk, we identify three main ways in which concepts are encountered and characterized: (i) numeric, (ii) symbolic, and (iii) granular. Each of these views come with their advantages and become complementary to some extent. The numeric concepts are built through various clustering techniques. The quality of numeric concepts evaluated at the numeric level is described by a reconstruction criterion. The symbolic description of concepts, which is predominant in the realm of Artificial Intelligence (AI), is represented by sequences of labels (integers): in this way their qualitative aspects are captured. This facilitates further qualitative stability analysis of concepts reflecting the bird’s-eye view of the data. The granular concepts augment numeric concepts by bringing information granularity into the picture and invoking the principle of justifiable granularity in their construction.
We elaborate on the general scheme of processing of granular modelling dwelling upon a collection of granular concepts and forming a collection of granular models.
Witold Pedrycz (IEEE Fellow, 1998) 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. In 2009 Dr Pedrycz was elected a foreign member of the Polish Academy of Sciences. In 2012 he was elected a Fellow of the Royal Society of Canada. In 2007 he received a prestigious Norbert Wiener award from the IEEE Systems, Man, and Cybernetics Society. He is a recipient of the IEEE Canada Computer Engineering Medal, a Cajastur Prize for Soft Computing from the European Centre for Soft Computing, a Killam Prize, and a Fuzzy Pioneer Award from the IEEE Computational Intelligence Society.
His main research directions involve Computational Intelligence, fuzzy modelling and Granular Computing, knowledge discovery and data science, pattern recognition, data science, knowledge-based neural networks, and Software Engineering. He has published numerous papers in this area with the current h-index of 102. He is also an author of 17 research monographs and edited volumes covering various aspects of Computational Intelligence, data mining, and Software Engineering.
Dr. Pedrycz is vigorously involved in editorial activities. He is an Editor-in-Chief of Information Sciences, Editor-in-Chief of WIREs Data Mining and Knowledge Discovery (Wiley), and Co-editor-in-Chief of Int. J. of Granular Computing (Springer) and J. of Data Information and Management (Springer). He serves on an Advisory Board of IEEE Transactions on Fuzzy Systems and is a member of a number of editorial boards of international journals.