Rough sets as a framework for data mining

The issues of Real World are: a) Very large data sets b) Mixed types of data (continuous valued, symbolic data) c) Uncertainty (noisy data) d) Incompleteness (missing, incomplete data) e) Data change f) Use of background knowledge The main goal of the rough set analysis is induction of approximations of concepts. Rough sets constitute a sound, basis for KDD. It offers mathematical tools to discover patterns hidden in data. It can be used for feature selection, feature extraction, data reduction, decision rule generation, and pattern extraction (templates, association rules) etc. Recent extensions of rough set theory have developed new methods for decomposition of large data sets, data mining in distributed and multi-agent systems, and granular computing.