Application of data decomposition to incomplete information systems

Many developed classification methods and knowledge discovery software, that were research subjects for years, suffer from the lack of possibility to handle data with missing attribute values. To adapt existing classification methods to incomplete information systems, we propose a decomposition method that allows more appropriate missing value attributes handling. The decomposition method consists of two phases. In the first step data from original decision table are partitioned into subsets. In the second step, knowledge from those subsets, that in our case is classification hypothesis, is combined to achieve a final classification based on a whole original decision table. There were carried out some experiments in order to evaluate the decomposition method.