期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2022
卷号：13
期号：5
DOI：10.14569/IJACSA.2022.0130525
语种：English
出版社：Science and Information Society (SAI)
摘要：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.
关键词：Data clustering; Kriging method; k-means algorithm; interpolation model
