Performing cluster analysis and discrimination analysis of hydrological factors in one step

Based on self-organizing map, a method that can perform cluster analysis and discrimination analysis in one step is proposed in this paper. Using the proposed method, one can view the relative topological relationships of input patterns, determine the proper number of clusters, and assign unknown patterns to known clusters without losing any information of input patterns. Regarding the capability of determining the proper number of clusters, the proposed method is superior to conventional cluster analysis. The discrimination results also show that the assignments of unknown patterns to known clusters are reasonable using the proposed method. The advantages of the proposed method are also demonstrated by an application to the hydrological factors affecting low-flow duration curves in southern Taiwan.