Computational Intelligence for Data Mining.


Web page in preparation for a
2-hour tutorial to be presented at

2002 World Congress on Computational Intelligence,
Honolulu, Hawaii, 12-17 May 2002

UMK - logo


Wlodzislaw Duch
Krzysztof Grabczewski
Norbert Jankowski,
Antoine Naud

wduch

Department of Informatics,
Nicolaus Copernicus University,

Grudziadzka 5, 87-100 Torun, Poland.

http://www.fizyka.umk.pl/kmk


This tutorial presents computational intelligence approach to data mining, stressing the need for understanding of the data structure. At each step computer programs will be used in real-time on real-world examples to illustrate various procedures involved.

  1. Forms of useful knowledge are discussed, including logical rules (crisp and fuzzy), decision trees, prototype-based rules and visualization techniques. The need for and advantages of various types of data analysis is explained.
  2. A short description of the philosophy of integration of algorithms used in our GhostMiner software is presented, including an outline of 6 algorithms used in the software: IncNet, FSM, SSV, kNN, MDS and the committees of models.
  3. Crisp and fuzzy logical rules are extracted from a few datasets. A tradeoff between accuracy/simplicity is explained using logical rules generated by FSM and SSV models.
  4. A method for optimization of logical rules derived from these systems, exploring the tradeoff between rejection/error level, is presented.
  5. A method to compute classification probabilities from any black-box system is presented. Assuming Gaussian uncertainties of measurements and crisp logical rules leads to analytical formulas that allow to optimize large complex sets of logical rules using gradient procedures. In effect interpretation is easy and accuracy is high.
  6. Visualization of data and visualization of decision borders of classifiers is presented as an alternative method of data understanding; an example combining IncNet neural network and interactive multidimensional scaling (MDS) is presented.
  7. Lessons from applications of this approach to a few real life problems are analyzed and simple logical rules for many datasets provided.

References:
Duch W, Adamczak R, Grabczewski K, Methodology of extraction, optimization and application of crisp and fuzzy logical rules. IEEE Transactions on Neural Networks, 12 (2001) 277-306


CV of the main presenter:

Wlodzislaw Duch is a professor of theoretical physics and applied computational sciences, since 1990 heading the Department of Informatics (formerly called a Department of Computer Methods) at Nicolaus Copernicus University, Torun, Poland. His degrees include habilitation (D.Sc. 1987) in many body physics, Ph.D. in quantum chemistry (1980), and Master of Science diploma in physics (1977) at the Nicolaus Copernicus University, Poland.
He has held a number of academic positions at universities and scientific institutions all over the world. These include longer appointments at the University of Southern California in Los Angeles, and the Max-Planck-Institute of Astrophysics in Germany (every year since 1984), and shorter (up to 3 month) visits to the University of Florida in Gainesville; University of Alberta in Edmonton, Canada; Meiji University, Kyushu Institute of Technology and Rikkyo University in Japan; Louis Pasteur Universite in Strasbourg, France; King's College London in UK, to name only a few.

He has been an editor of a number of professional journals, including IEEE Transactions on Neural Networks, Computer Physics Communications, Int. Journal of Transpersonal Studies and a head scientific editor of the "Kognitywistyka" (Cognitive Science) journal. He has worked as an expert for the European Union science programs and for other international bodies. He has published 4 books and over 250 scientific and popular articles in many journals. He has been awarded a number of grants by Polish state agencies, foreign committees as well as European Union institutions.

His full CV is here.