Cluster validation methods for localization of spatial rainfall data in the northeast region of Thailand

In order to develop spatial interpolation models for a large area, localization is required to partition global spatial data into local areas. Fuzzy c-means clustering are normally used to perform this task. However, the method needs prior information to determine the most proper number of cluster. This study proposes the use of two cluster validation methods, statistic-based method and simulation-based method to determine the optimal number of cluster for spatial data. The statistic-based method analyzes standard deviation of spatial data to determine the number of cluster, whereas the simulation-based method analyzes the training performance of artificial neural network. The proposed methods were applied to the spatial rainfall data in the northeast region of Thailand. The experimental results demonstrated that the proposed methods could provide reasonable results. The statistic-based method is statistically explainable for human analysts, whereas the simulation-based is an easy-to-use technique for cluster validation.