The Performance of Spectral Clustering Algorithms on Water Distribution Networks: Further Evidence

Abstract

The aim of the current paper is to revisit the performance of spectral clustering algorithms for water distribution networks. In the literature, there have been attempts to introduce improved algorithms based on graph theory. We focus on a class of these algorithms that applies the concepts of the spectral clustering approach. We assess the performance of spectral clustering algorithms on a wider range of water network types (i.e. large, medium, and small sized networks) using a wider range of clustering methods (both partitioning and hierarchical) and performance indicators. Our findings suggest that partitioning methods, such as k-means are not consistently efficient in all types of networks. Nonetheless, the Partitioning Around Medoids (PAM) algorithm shows a relatively good performance according to modularity, while the internal indices of k-means and hierarchical clustering algorithms are more efficient. Stability indices show that PAM and CLARA algorithms are more efficient.