Enhanced equal frequency partition method for the identification of a water demand system

This paper deals with unsupervised partitioning. A first goal of this paper is to present an enhancement to the Equal Frequency Partition (EFP) method that allows to reduce, to some extent, the main drawback of this classical classification method, i.e. the data distribution dependency. A second goal of this work is to use the Enhanced Equal Frequency Partition (EEFP) method within the discretization process of the Fuzzy Inductive Reasoning (FIR) methodology for the identification of a model of a water demand system. It is shown that use of the EEFP method allows to obtain more accurate FIR models of the water demand system, reducing the prediction errors.