In recent years, we have observed rapid progress in research on data mining using rough sets. Rough set theory, invented by Zdzislaw Pawlak in 1982, is especially well-suited for research in data mining and related areas such as granular computing, intelligent information systems, nonclassical logics, web mining, and uncertainty reasoning.
The scope of the International Conference on Rough Sets and Current Trends in Computing (RSCTC 2008) held in Akron, Ohio, included all of these aspects of innovative theories, methodologies, and applications of rough set theory.
Five articles presented at the RSCTC 2008 were selected for publication in the International Journal of Intelligent Systems. An article on experiments with rough set approach to face recognition by X. Chen and W. Ziarko reports results of experiments on face recognition based on variable precision rough sets. The objective of this article was to explore a methodology to select the most discriminative features from facial photographs and to develop a decision-table classifier to achieve high face recognition rate. Another article on probabilistic rough sets (Probabilistic rule induction with the ERS data mining system) by J. W. Grzymala-Busse and Y. Yao presents a methodology for changing input data sets so that LERS (Learning from Examples based on Rough Sets), a standard rough set theory-based data mining system, can induce positive, boundary, and possible rule sets associated with two typical parameters, alpha and beta. A class of dynamic rough partitive algorithms by G. Peters and R. Weber discusses three rough partitive algorithms and extends these algorithms by dynamic elements. A new class of dynamic rough partitive algorithms is applicable to data sets with a variable data structure. The article (Granular computing in the frame of rough mereology. A case study on the classification of data into decision categories by means of granular reflections of data) by L. Polkowski and P. Artiemjew presents applications of a granular reflection of data using rough mereology. The authors illustrate their ideas by reporting results of experiments on a well-known data set on breast cancer from Wisconsin. Finally, the article entitled from data to classification rules and actions by Z. W. Ras and A. Dardzinska discusses action rules, defined as describing possible transitions of objects from one state to another with respect to some given attribute. Such rules