Abstract
The rough sets theory has proved to be useful in knowledge discovery from databases, decision-making contexts and pattern recognition. However this technique has some difficulties with complex data due to its lack of flexibility and excessive dependency on the initial discretization of the continuous attributes. This paper presents the divisible rough sets as a new hybrid technique of automatic learning able to overcome the problems mentioned using a combination of variable precision rough sets with self-organizing maps and perceptrons. This new technique divides some of the equivalence classes generated by the rough sets method in order to obtain new certain rules under the data which originally were lost. The results obtained demonstrate that this new algorithm obtains a higher decision-making success rate in addition to a higher number of classified examples in the tested data sets.
