An approach to clustering using the expectation-maximization and selection of attributes relieff applied to water treatment plants process

Abstract

The water treatment process contains several physico-chemical parameters relevant to decision making and the water quality scenarios’ identification. Some scenarios are evident and can be observed without the application of mathematical or statistical techniques, however some of these scenarios are difficult to distinguish, and it is necessary to use computational intelligence techniques for solution. In this context, the paper aims to show the application of the expectation-maximization (EM) algorithm for data clusters of the coagulation process and the ReliefF algorithm to determine the importance of the physico-chemical parameters, using the WEKA tool to analyze historical dataset of a water treatment plant. The results were favorable to the scenarios’ identification and to determine the relevance of the parameters related to the process.