Information management relies on knowledge acquisition methods for extraction of knowledge from data. Statistical methods traditionally used for data analysis are satisfied with predictions, while understanding of data and extraction of knowledge from data are challenging tasks that have been pursued using computational intelligence (CI) methods. Recent advances in applications of CI methods to data understanding are presented, implementation of methods in the GhostMiner data mining package developed in our laboratory described, new directions outlined and challenging open problems posed. To illustrate the advantages of different techniques, a single dataset is exposed to the many-sided analysis.

Keywords: data understanding, knowledge extraction, decision support, data mining, computational intelligence, machine learning, neural networks, feature extraction, decision trees.

Preprint for comments in PDF, 171 KB.
Reference: Duch W, Jankowski N, Grabczewski K, Computational intelligence methods for information understanding and information management. The 4th International Conference on Information and Management Sciences (IMS2005), July 1-10, 2005, Kunming, China, Vol. 4. Series of Information and Management Sciences, California Polytechnic State University, pp. 281-287

BACK to the publications of W. Duch.
BACK to the on-line publications of the Department of Informatics, NCU.