Cluster discovery in environmental databases using GESCONDA: The added value of comparisons
Clustering techniques have a great importance in knowledge discovery because they can find out new groups or clusters of objects within databases. Thus, they are unsupervised learning methods, very useful when facing unknown, unlabelled and ill-structured databases, as environmental databases are. In this paper, different clustering algorithms are analyzed and compared. They are used on a real environmental data set in order to study their impact in characterizing states in this kind of domains. The comparison of the methods is undertaken using the system GESCONDA, which is a prototype of a data mining tool. Environmental data used in this paper are from a Catalan wastewater treatment plant and refers to different variables of the plant at different spatial points along 149 days.
clustering environmental databases data mining wastewater treatment plant knowledge acquisition and management cluster validation machine learning statistical modelling