Self-generation of control rules using hierarchical and nonhierarchical clustering for coagulant control of water treatment plants

Abstract

In coagulant control of water treatment plants, rule extraction, one of datamining categories, was performed for coagulant control of a water treatment plant, Clustering methods were applied to extract control rules from data. These control rules can be used for fully automation of water treatment plants instead of operator's knowledge for plant control. In this study, statistical indices were used to determine cluster numbers and seed points from hierarchical clustering. These statistical approaches give information about features of clusters, so it can reduce computing cost and increase accuracy of clustering. The proposed algorithm can play an important role in datamining and knowledge discovery. © Springer-Verlag Berlin Heidelberg 2005.