Dynamic data discretization technique based on frequency and K-Nearest Neighbour algorithm

In this paper we propose a new approach to the dynamic data discretization technique. The technique is called frequency dynamic interval class (FDIC). FDIC consists of two important phases: The dynamic intervals class phase and the interval merging phase. The first phase uses a simple statistical frequency measure to obtain the initial intervals while in the second phase a K-nearest neighbour is used to calculate the merging factor for the unknown intervals. The experimental results showed that FDIC generates more intervals in an attribute, and less number rules with comparable accuracies within three tested datasets. It indicates that FDIC managed to reduce the loss of knowledge in several other techniques that generated the very least number of intervals.