Knowledge discovery using clustering analysis of rainfall timeseries

Rainfall time series analysis using clustering involves the identification of temporal patterns, with each data item representing an individual storm. This analysis results in clusters of data items that trend in a common way and can be utilized in stochastic simulation, water resources planning and the identification of future directions due to climate change. A comparative analysis is carried out of several methods that use intra versus inter-cluster distances, for the estimation of the relevant number of clusters using a big dataset of the described rainfall time series. Visualization using topographic maps that are produced via nonlinear projection techniques is applied, to validate the presence of both distance and density structures and to assist in the final determination of the numbers of clusters. This stands in contrast to empirical and not completely data-driven approaches of the literature, in which constrained clustering methods are employed with assumptions on the presence of four classes.