Plenary Invited Speaker, October 10, 2006

Neural Networks for
Selected Data Mining Tasks

Dr. Jacek M. Zurada

Samuel T. Fife Professor

Department of Electrical and Computer Engineering

University of Louisville, Louisville, KY 40292, USA

SUMMARY: The opening part of the talk introduces basic premises of data mining. It is shown how paradigms of neurocomputing prove useful and effective for data mining. These are data-driven modeling, feature extraction, dimensionality reduction, visualization, knowledge extraction and logic rule discovery. Such modeling often involves handling of heterogenous, subjective, imprecise and noisy data. This part concludes with providing overview of various model-building tasks which can be performed by neural networks.

The second part of the presentation outlines the concept of dimensionality reduction of the input data vectors. This technique leads to reduced models achieved through evaluation of sensitivity matrices of perceptron networks. When developing reduced models it is also useful to eliminate underutilized internal weigts and, possibly, also neurons via pruning techniques. The concluding part of the talk reviews the capabilities of perceptron networks for producing understandable IF-THEN rules.

SPEAKER: Dr. Jacek M. Zurada is the S.T. Fife Alumni Professor of Electrical and Computer Engineering at the University of Louisville, Louisville, Kentucky, USA. He is the author of the 1992 PWS text Introduction to Artificial Neural Systems, co-editor of the 1994 IEEE Press volume Computational Intelligence: Imitating Life, and of the 2000 MIT Press book Knowledge Based Neurocomputing. He is also the author or co-author of more than 270 journal and conference papers in the area of neural networks, computational intelligence, and analog and digital VLSI circuits. In 1998-2003 Dr. Zurada was the Editor-in-Chief of IEEE Transactions on Neural Networks. In 2004-05 he served as the IEEE Computational Intelligence Society President. He is an IEEE Fellow and CIS Distinguished Speaker.