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"A Primer on Cluster Analysis: I. Models and Algorithms” by James C. Bezdek sponsored by the IEEE Computational Intelligence Society from the IEEE World Congress on Computational Intelligence (WCCI) This course- the first in a series of three - provides a foundation for understanding the field of cluster analysis in unlabeled data. The target audience for this course comprises undergraduate and graduate students majoring in engineering and science, as well as practicing engineers and scientists interested in either research about or applications of clustering to real world problems such as data mining, image analysis and bioinformatics. The subject matter is widely available in a number of standard textbooks given in the references below. The course begins with a discussion of the general nature of clustering. Three problems are identified: tendency assessment, partitioning and validation. Two types of data are discussed: object vector data, and pair wise objects relational data. Next, I develop the mathematical structure needed to carry clustering algorithms, discussing the notions of similarity, label vectors, partition matrices (U) and point prototypes (V). The second part of the course contains a description (and pseudo code) for one algorithm each from the four major categories of clustering methods. Specifically, I discuss and illustrate with a numerical example: (i) the U only model for single linkage clustering; (ii) the V only model for clustering with Kohonen's self-organizing map; (iii) the (U,V) model for clustering with the hard and fuzzy c-means models; and (iv) the (U,V,+) model for clustering using the expectation-maximization algorithm for Gaussian mixture decomposition. After completing this course you should be able to develop an understanding of:
Jim received the BS in Civil Engineering from U. of Nevada (Reno, 1969); and the PhD in Applied Mathematics Cornell University, 1973. Jim's interests include woodworking, optimization, motorcycles, pattern recognition, fine cigars, fishing, image processing, computational neural networks, blues music, and clustering in very large data sets. Jim is past president of NAFIPS, IFSA and the IEEE NNC (aka CIS), is the founding editor of the Int'l. Journal Approximate Reasoning and the IEEE Transactions on Fuzzy Systems, is a fellow of the IEEE and IFSA, and is a recipient of the IEEE 3rd Millennium , IEEE Fuzzy Systems Pioneer and IEEE Frank Rosenblatt medals.
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"A Primer on Clustering: II. Tendency Assessment and Cluster Validity" by James C. Bezdek sponsored by the IEEE Computational Intelligence Society from the IEEE International Joint Conference on Neural Networks This course - the second in a series of three - discusses several approaches to the first and third problems of clustering identified in module I - viz., pre-clustering tendency assessment and post-clustering cluster validation. The target audience comprises advanced undergraduate and graduate students majoring in engineering and science, and practicing engineers and scientists interested in either research about or applications of clustering to real world problems such as data mining, image analysis and bioinformatics. Some of subject matter in this course is available in textbooks (most notably some of the material about cluster validity functionals), and some of the subject matter is the object of (my) current research. The references contain pointers to some excellent papers on these TOPics, and on a number of related or competitive methods that have been proposed and studied by others. I begin with a simple numerical example that establishes the necessity for both assessment and validity. Then, I discuss the visual assessment of tendency family of algorithms (VAT, sVAT and coVAT). These algorithms produce images that enable a user to make useful guesses about the number of clusters to seek in relational data before proceeding with a partitioning method for finding the clusters. Since object data can always be converted to relational form by computing pair wise distances, these methods are well defined for all types of unlabeled numerical data. The coVAT algorithm provides a means for estimating the number of clusters in each of the four problems associated with rectangular relational data: row clusters, column clusters, joint (pure) clusters, and mixed co-clusters. The second half of this course presents some examples of cluster validation using scalar measures or indices of cluster validity. Several examples from each of the three major categories (crisp, fuzzy and probabilistic) of indices are presented. This course concludes with a numerical example that compares 23 indices of all three types on clusters in 12 sets of data drawn from mixtures of Gaussian distributions having either 3 or 6 components. (SOME) indices of all three types do pretty well in this example, while others do very badly. I don't think this problem has a general "solution", but since we use clustering in many, many applications, we keep trying to find good indices to validate algorithmic outputs. After completing this course you should be able to develop an understanding of:
Jim received the BS in Civil Engineering from U. of Nevada (Reno, 1969); and the PhD in Applied Mathematics Cornell University, 1973. Jim's interests include woodworking, optimization, motorcycles, pattern recognition, fine cigars, fishing, image processing, computational neural networks, blues music, and clustering in very large data sets. Jim is past president of NAFIPS, IFSA and the IEEE NNC (aka CIS), is the founding editor of the Int'l. Jo. Approximate Reasoning and the IEEE Transactions on Fuzzy Systems, is a fellow of the IEEE and IFSA, and is a recipient of the IEEE 3rd Millenium , IEEE Fuzzy Systems Pioneer and IEEE Frank Rosenblatt medals.
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"A Primer on Clustering: III. Fuzzy Cluster Analysis in Very Large Scale Data Sets" by James C. Bezdek sponsored by the IEEE Computational Intelligence Society from the IEEE International Joint Conference on Neural Networks This last module in the series discusses just one approach to the interesting and important problem of clustering in very large (VL) data. The target audience is graduate students majoring in engineering and science, and practicing engineers and scientists interested in either research about or applications of clustering applied to very large real world problems that occur in data mining, image analysis and bioinformatics. Almost none of the subject matter in this course is available in textbooks; almost all of it is the object of (my own) current research, and as such, it reflects my own bias, prejudices, background and interests. I have supplied references that contain pointers to many nice papers on these TOPics that use related or competitive methods that have been proposed and studied by others. I begin with a characterization of VL data. For me, this means any data set that you cannot load into your computer. Not an objective definition, but a definition that is easy to understand and practical, because there is a data set too big for any computer you use, and hence, VL for you. There are two main approaches to clustering in VL data; distributed clustering, and progressing sampling followed by extension. I discuss the first approach briefly, but it seems much more difficult to me than the second approach. Next, I define progressive sampling followed by (non-iterative) extension. This idea is pretty general: it can accelerate most (but not all) iterative algorithms that estimate parameters with loadable data (this is true for both clustering and classifier design!), and, it provides a means for approximating the outputs of many algorithms for unloadable data. So, one of the main points of this third course is to establish the basic ideas of progressive sampling and extension. The method of clustering in VL data by (sampling + extension) is developed and illustrated with four clustering algorithms: (i) extended fast fuzzy c-means (eFFCM) for segmentation of VL images; generalized fast fuzzy c-means (geFFCM) for clustering in VL object data (VL sets of feature vectors in p dimensions); (iii) generalized fast expectation maximization (geFEM) for clustering by Gaussian mixture decomposition in VL object data; and (iv), extended non-Euclidean relational fuzzy c-means (eNERF) for clustering in VL (square) relational data. These four methods are presented in the spirit of active research - i.e., parts of them clearly need improvement and more testing, and I expect much of this material to be replaced by better approaches as our understanding of clustering using this approach matures. After completing this course you should be able to develop an understanding of:
Jim received the BS in Civil Engineering from U. of Nevada (Reno, 1969); and the PhD in Applied Mathematics Cornell University, 1973. Jim's interests include woodworking, optimization, motorcycles, pattern recognition, fine cigars, fishing, image processing, computational neural networks, blues music, and clustering in very large data sets. Jim is past president of NAFIPS, IFSA and the IEEE NNC (aka CIS), is the founding editor of the Int'l. Jo. Approximate Reasoning and the IEEE Transactions on Fuzzy Systems, is a fellow of the IEEE and IFSA, and is a recipient of the IEEE 3rd Millenium , IEEE Fuzzy Systems Pioneer and IEEE Frank Rosenblatt medals.
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"Computational Intelligence: Natural Information Processing" by Leonid Perlovsky, sponsored by the IEEE Computational Intelligence Society This course covers the rapidly evolving field of Computational Intelligence and focuses on the current understanding of the fundamental principles of working the mind, their computational implementations, and practical applications. This course covers mind mechanisms, including concepts, emotions, instincts, behavior, language, cognition, understanding, thinking, intuitions, conscious and unconscious, abilities for formation of symbols and aesthetic feelings. Computational techniques are given for these mechanisms and abilities. The goal of this course is to provide a basic mathematical understanding of the working of the mind. Its secondary goal is to demonstrate practical applications of these mechanisms for pattern recognition, tracking, fusion, search engines, and for integrated systems combining sensor signals and communication data. Lastly, this course will outline future research directions. Historical and current difficulties in developing intelligent systems (IS) and applications will be discussed along with how the mind and new computational techniques overcome these difficulties. By the end of this course, learners will be familiar with several general applications addressed by IS, computational difficulties encountered over fifty years, and basic novel approaches to overcoming these difficulties. After completing this course you should be able to develop an understanding of:
Dr. Leonid Perlovsky is Principal Research Physicist and Technical Advisor at the Air Force Research Laboratory/SNHE.
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"Implementations of Computational Intelligence Techniques" by Vincenzo Piuri and Fabio Scotti sponsored by the IEEE Computational Intelligence Society Computational Intelligence techniques are a powerful and adaptable approach to tackle problems and cases for which the conventional technologies have not been proved sufficiently effective. These results are achieved by mimicking some aspects of the knowledge representation and processing performed by the brain. The computational efforts implied by these approaches are usually quite relevant. Practical use of computational Intelligence technologies is constrained by limits (e.g., on performance, cost, accuracy) imposed by the envisioned application. The choice of the most appropriate implementation approach becomes therefore fundamental in order to find best balance among the computational intelligence characteristics and the application constraints. A methodological perspective becomes also useful in order to tackle the design of a dedicated system encompassing computational Intelligence components as most efficiently as possible. After completing this course you should be able to develop an understanding of:
Vincenzo PIURI obtained the Ph.D. in Computer Engineering in 1989, at Politecnico di Milano, Italy. From 1992 to September 2000, he was Associate Professor in Operating Systems at Politecnico di Milano. Since October 2000 he is Full Professor in Computer Engineering at the University of Milano, Italy. He was Visiting Professor at the University of Texas at Austin during the summers from 1993 to 1999. Fabio Scotti received the Dr. Ing. degree in electronic engineering in 1998 from Politecnico di Milano, Milano, Italy, where he is currently pursuing the Ph.D. degree. His research interests include signal and image processing, neural technologies for image processing, and optical technologies.
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"Information Theoretic Learning" by Jose C. Principe, sponsored by the IEEE Computational Intelligence Society This course examines Information Theory and our efforts to develop an information-theoretic criterion which can be utilized in adaptive filtering and neurocomputing. The main aim of our research is to develop new signal processing techniques by going beyond the basic assumptions of Linearity, Gaussianity and Stationarity. By capturing higher order statistics of data using Information Theory, we solve a variety of problems in Biomedical Signal Processing, Communications and Machine Learning. In the context of Information Theory, “information” is a precise, fully characterized mathematical quantity. The core process toward learning from examples in both biological and artificial systems is extracting information directly from data. The concept of learning from examples begins with a data set which globally conveys information about a real-world event, with the goal of capturing the information in the parameters of a learning machine. The information exists in a “distributed” mode in the data set, and, after successful training, is “condensed” in the parameters of the learning machine. Here, we develop information-theoretic criteria which can train directly from samples of linear or nonlinear mappers, either for entropy or mutual information maximization or minimization. After completing this course you should be able to develop an understanding of:
Jose C. Principe is Distinguished Professor of Electrical and Biomedical Engineering at the University of Florida, Gainesville, where he teaches advanced signal processing and artificial neural networks (ANNs) modeling. He is BellSouth Professor and Founder and Director of the University of Florida Computational NeuroEngineering Laboratory (CNEL).
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“Introduction to Multilayer Perceptrons” by Marco Gori, sponsored by the IEEE Computational Intelligence Society The course introduces multilayer perceptrons in a self-contained way by providing motivations, architectural issues, and the main ideas behind the Backpropagation learning algorithm. In addition, the course shows how multilayer perceptrons can be successfully used in real-world applications After completing you should be able to develop an understanding of:
Marco Gori received the Laurea in electronic engineering from University of Florence, Italy, in 1984, and the Ph.D. degree in 1990 from University of Bologna, Italy, working partially as a visiting student at the School of Computer Science (McGill University, Montreal). In 1992, he became an associate professor at University of Florence and, in November 1995, he joined the University di Siena, where he is currently full professor of computer science. Dr. Gori is a fellow of the IEEE and of the ECCAI.
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“Introduction to Type-2 Fuzzy Sets and Systems” by Jerry Mendel, sponsored by the IEEE Computational Intelligence Society This course will provide an introduction to, and an overview of, type-2 fuzzy sets (T2 FSs) and systems. It will locate type-2 fuzzy sets and systems in an educational taxonomy, so that the learner will appreciate from the onset the importance of studying such fuzzy sets; explain what a T2 FS is, how it is different from a type-1 FS, and why it is needed. The course will also provide careful definitions and pictures of the new terminology of T2 FSs and explain the importance of interval type-2 fuzzy sets over more general T2 FSs. The course will also explain important representations for a T2 FS (one is very good for computing, and another is very good for quickly developing the structure of the solution to a new theoretical problem) After completing you should be able to develop an understanding of:
Jerry M. Mendel received the Ph.D. degree in electrical engineering from the Polytechnic Institute of Brooklyn, Brooklyn, NY. Currently he is Professor of Electrical Engineering and Systems Architecting Engineering at the University of Southern California in Los Angeles, where he has been since 1974. He has published over 470 technical papers and is author and/or editor of eight books.
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“Methods & Models of Collaborative Computational Intelligence” by Witold Pedrycz sponsored by the IEEE Computational Intelligence Society There are rapidly emerging needs to deal with distributed sources of data (sensors and sensor networks, web sites, databases). While recognizing their limited accessibility at a global level (associated with technical constraints and/or privacy issues) and fully acknowledging benefits of collaborative processing, we propose a concept of Collaborative Computational Intelligence (CI), and collaborative fuzzy models, in particular. The variety of possible mechanisms of interaction is organized into a setting of the C3 interaction paradigm (communication – collaboration – consensus). This helps us offer a coherent taxonomy of various schemes of interaction which in the sequel implies a certain development of a suite of algorithms. In this setting, the role granular information in the establishing of the mechanisms of interaction plays a pivotal role. We consider distributed fuzzy models and fuzzy modeling. In particular, we elaborate on the key design issues concerning fuzzy rule-based systems with local functional models occurring at their conclusion parts and show how the fundamental modes of interaction are exploited here. It will be demonstrated that more advanced constructs such as type-2 fuzzy sets emerge naturally in distributed fuzzy modeling and come with a well-defined semantics of their membership functions by being fully reflective of the character of the underlying distributed data. In the context of collaborative fuzzy modeling, we bring forward a concept experience–consistent fuzzy system identification showing how fuzzy models built on a basis of limited data can benefit from taking advantage of the past experience conveyed in the form of previously constructed fuzzy models. Detailed algorithmic considerations embrace several design scenarios in which we apply the mechanism of experience consistency at the level of conditions and conclusions of the rules. We also show that a level of achieved experience-driven consistency can be quantified through fuzzy sets (fuzzy numbers) of the parameters of the local models standing in the conclusion parts of the rules this leading to the emergence of granular constructs of fuzzy modeling. After completing you should be able to develop an understanding of:
Witold Pedrycz received the M.Sc., and Ph.D., D.Sci. all from the Silesian University of Technology, Gliwice, Poland. He is a Professor and Canada Research Chair (CRC) in Computational Intelligence in the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada. Dr. Pedrycz is an IEEE Fellow and IFSA Fellow. His main research interests encompass fundamentals of Computational Intelligence, Granular Computing, fuzzy modeling, knowledge discovery and data mining, fuzzy control including fuzzy controllers, pattern recognition, knowledge-based neural networks, relational computing, and Software Engineering. He has published vigorously in these areas. He is an author of 11 research monographs and over 250 journal papers published in highly reputable journals. His research is highly cited and he is also on the list Highly cited researcher on ISI HighlyCited.com. Witold Pedrycz has been a member of numerous program committees of IEEE conferences in the area of Computational Intelligence, Granular Computing, fuzzy sets and neurocomputing. He was a Program Chair of the 2007 Int. Conf on Machine Learning and Cybernetics, August 19-22, 2007, Hong Kong. He was also a General Chair of NAFIPS 2004, June 24-26, 2004, Banff, Alberta- a flagship conference of the NAFIPS Society. He currently serves as Editor-in-Chief of IEEE Transactions on Systems Man and Cybernetics-part A. He is an Associate Editor of Transactions on Fuzzy Systems. He is also on editorial boards of over 10 international journals. Dr Pedrycz is also an Editor-in-Chief of Information Sciences. NEXT Dr. Pedrycz is the past president of IFSA and the past president of NAFIPS. Dr. Pedrycz is a recipient of the prestigious Norbert Wiener Award which is one of the two highest awards of the IEEE Systems, Man, and Cybernetics Society. He is also a recipient of the K.S. Fu from NAFIPS.
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“Type-2 Fuzzy Logic Controllers: Towards a New Approach for Handling Uncertainties in Real World Environments” by Hani Hagras, sponsored by the IEEE Computational Intelligence Society This course will have a large impact on a large audience as handling uncertainties will be a very important challenge to any real world application that operate in real world changing and dynamic environments. The course will present the theoretical aspects of type-2 FLCs and how to build a type-2 FLC. The course will also present many applications in different areas ranging from Control of Marine Diesel Engines, Autonomous Outdoor mobile Robots as well as Embedded Agents and Ambient Intelligent Environments which deals with how we can embed very efficient computational intelligence and type-2 techniques in small computing and memory platforms. After completing you should be able to develop an understanding of:
Prof. Hani Hagras is a Professor of Computer Science, Director of the Centre for Computational Intelligence and leader of the Fuzzy Systems Research Group in the University of Essex, UK.
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