Pendiskretan data set kasar menggunakan ta’akulan boolean - Rokiah @ Rozita Ahmad; Maslina Darus; Siti Mariyam Shamsuddin; Azuraliza Abu Bakar (Penerbit UKM, 2004)
Data discretization of rough set towards real attribute values is one of the important aspect in the data mining concepts, particularly the ones which involved classification problems. Emphirical results showed that the quality of the classification depends on the discretization algorithm used in the input data pre-processing phase. In general, discretization is a process of search-ing for partition of attribute domains into intervals and unifying the values over each interval. Discretization process involves searching of cuts which determine the intervals acquired. All values which lie within each interval are mapped to the same values, in effect converting numerical attributes that can be treated as being symbolic. The search for cuts is performed on the internal integer representation of the input decision system. This paper describes the role of rough set with Boolean reasoning discretization process in converting the real values of printed mathematical symbol that leads to the better recognition rates using neural network