Research on k-Fermat algorithm for optimal partitioning clustering of high-dimensional data
In this paper,the optimal rally point of two-dimensional data(generalized Fermat point)in the number theory is extended to a high-dimensional space,and a partitioning clus-tering algorithm called the k-Fermat clustering algorithm is proposed,with a high-dimensional Fermat point as the optimal clustering center point.Firstly,the optimality and uniqueness of Fermat points as cluster centers are analyzed theoretically,secondly,an algorithmic system for solving Fermat point of high-dimensional data and guiding the clustering process by the plant growth simulated algorithm(PGSA)is established,and finally,the complexity of the algorithm is proved rigorously.In order to verify the performance of the algorithm,the k-Fermat algo-rithm is compared with dozens of mainstream clustering algorithms published in recent years in international important journals and top conferences using the classical data sets published internationally,and the accuracy and stability of the algorithm in this paper are verified.This paper improves the theoretical deficiency that there is no optimal clustering center in current partitioning clustering algorithms,and explores a new approach for unsupervised learning.
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