A k-interpolation Model Clustering Algorithm based on Kriging Method

In this work, a k-interpolation model clustering algorithm is proposed based on Kriging method, aim to partition data according to the relationship between the response of interest and input variables. Kriging method is used to describe the relationship between the response of interest and input variables. For each datum, the estimation errors of the interpolation models of the clusters are used to decide its assignment. An optimization strategy is proposed to obtain the final clustering results. The key factors of the proposed algorithm on its performance are studied through the synthetic and real-world datasets. The results show that the proposed algorithm is able to cluster the data according to the response of interest and input variables, and provides competitive clustering performance compared with the other clustering algorithms.

@article{Chen2022,
  title     = {A k-interpolation Model Clustering Algorithm based on Kriging Method},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {5},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Guoyan Chen and Yaping Qian},
  doi       = {10.14569/IJACSA.2022.0130525},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130525}
}