  This paper introduces the innovative Power Muirhead Mean K-Nearest Neighbors
(PMM-KNN) algorithm, a novel data classification approach that combines the
K-Nearest Neighbors method with the adaptive Power Muirhead Mean operator. The
proposed methodology aims to address the limitations of traditional KNN by
leveraging the Power Muirhead Mean for calculating the local means of K-nearest
neighbors in each class to the query sample. Extensive experimentation on
diverse benchmark datasets demonstrates the superiority of PMM-KNN over other
classification methods. Results indicate statistically significant improvements
in accuracy on various datasets, particularly those with complex and
high-dimensional distributions. The adaptability of the Power Muirhead Mean
empowers PMM-KNN to effectively capture underlying data structures, leading to
enhanced accuracy and robustness. The findings highlight the potential of
PMM-KNN as a powerful and versatile tool for data classification tasks,
encouraging further research to explore its application in real-world scenarios
and the automation of Power Muirhead Mean parameters to unleash its full
potential.
