Fuzzy clustering for Optimally Weighted Cluster Kriging

Kriging or Gaussian Process Regression has been successfully applied in many fields. One of the major bottlenecks of Kriging is the complexity in both processing time (cubic) and memory (quadratic) in the number of data points. To overcome these limitations, a variety of approximation algorithms have been proposed. One of these approximation algorithms is Optimally Weighted Cluster Kriging (OWCK). In this paper, OWCK is extended and enhanced by the use of fuzzy clustering methods in order to increase the accuracy. Several options are proposed and evaluated against both the original OWCK and a variety of other Kriging approximation algorithms.