KNN and K-means in Gini Prametric Spaces, at ECAI 2025, in Bologna, Italy

Our paper, written with Cassandra Mussard, intern last summer, and Stéphane Mussard, entitled KNN and K-means in Gini Prametric Spaces will be presented this Fall at the 28th European Conference on Artificial Intelligence, ECAI 2025, that will take place on October 25-30, 2025, in Bologna, Italy.

This paper introduces innovative enhancements to the K-means and K-nearest neighbors (KNN) algorithms based on the concept of Gini prametric spaces. Unlike traditional distance metrics, Gini-based measures incorporate both value-based and rank-based information, improving robustness to noise and outliers. The main contributions of this work include: proposing a Gini-based measure that captures both rank information and value distances; presenting a Gini K-means algorithm that is proven to converge and demonstrates resilience to noisy data; and introducing a Gini KNN method that performs competitively with state-of-the-art approaches such as Hassanat’s distance in noisy environments. Experimental evaluations on 14 datasets from the UCI repository demonstrate the superior performance and efficiency of Gini-based algorithms in clustering and classification tasks. This work opens new avenues for leveraging rank-based measures in machine learning and statistical analysis.


OpenEdition suggests that you cite this post as follows:
Arthur Charpentier (July 11, 2025). KNN and K-means in Gini Prametric Spaces, at ECAI 2025, in Bologna, Italy. Freakonometrics. Retrieved December 7, 2025 from https://doi.org/10.58079/14c0s


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