Assessment of clustering algorithms in discriminating eutrophic levels in coastal waters

Abstract

Cluster analysis has been used widely as a tool for assessing eutrophic trends in coastal waters. The efficiency of clustering in discriminating between oligotrophic, mesotrophic and eutropic sites, depends on the variables used, the distance measure and the clustering algorithm applied. In the present work seven clustering algorithms were evaluated using sets of data from sampling sites of known water type. The results showed that only the Ward’s algorithm had high resolution in discriminating sampling sites of different trophic status. The remaining clustering algorithms did not show remarkable resolution in classifying different water types. The use of the Ward clustering algorithm is recommended in eutrophication studies where discrete clusters of oligotrophic, mesotrophic and eutrophic water type are under investigation.