Fast k-means with stable instance sets

Ariel Basso Madjoukeng, Edith Belise Kenmogne, Benoît Frénay

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Résumé

The k-means algorithm is used to group objects according to similarity or distance criteria. Due to its simplicity and operating principle, this algorithm has become one of the most widely used for clustering problems. However, despite its popularity, it has several limitations. One of these issues is the excessive convergence time on multidimensional datasets. To address this limitation, several fast variants of the k-means algorithm have emerged. Some optimize the process of updating centroids, while others optimize the process of assigning points to clusters and finally other methods propose to subdivide a dataset into batches and apply the algorithm to various batches to accelerate the speed of convergence of this algorithm. Despite
these advancements, existing approaches are not always very optimal, and many of them optimize the algorithm while losing the efficiency of the k-means algorithm. This work address those challenges and proposes a new fast variant of the k-means algorithm capable of significantly optimizing the convergence time of the k-means algorithm while maintaining the efficiency of the naive k-means algorithm. This paper proposes a multi-level optimization of this algorithm. It proposes an efficient heuristic
for determining stable and variant points from an iteration. Additionally, it optimizes the process of updating the centroid calculation process, which until now has been done using all the points in the cluster. Finally, it optimizes the assignment process
using the notion of neighborhood clusters.
langue originaleAnglais
titre2024 International Joint Conference on Neural Networks, IJCNN 2024 - Proceedings
EditeurIEEE
ISBN (Electronique)979-8-3503-5931-2
ISBN (imprimé)979-8-3503-5932-9
Les DOIs
Etat de la publicationPublié - 16 sept. 2024

Série de publications

NomProceedings of the International Joint Conference on Neural Networks

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