2.1.4. Summary and Comparison of Clustering Algorithms

In general, it is difficult to recommend a single algorithm as being the most suitable for clustering, particularly with data that is uncertain and of poor quality, such as the features of pipe flow or water level data used here [41]. It is, therefore, advisable to use several algorithms and compare their performance for specific applications. Here, we use KC, SC, and AC to discover the unknown subgroups in simulated water depth data of UDSs' junctions. Table 1 summarizes the advantages and disadvantages of these algorithms from review papers [24,33,44].


