Incremental clustering based on Wasserstein distance between histogram models
Xiaotong Qian, Guénaël Cabanès, Parisa Rastin, Mohamed Alae Guidani, Ghassen Marrakchi, Marianne Clausel, Nistor Grozavu · Pattern Recognition · 2025
In this article, we present an innovative clustering framework designed for large datasets and real-time data streams which uses a sliding window and histogram model to address the challenge of memory congestion while reducing computational complexity and improving cluster quality for both static and dynamic clustering. The framework provides a simple way to characterize the probability distribution of cluster distributions through histogram models, regardless of their distribution type. This advantage allows for efficient use with various conventional clustering algorithms . To facilitate effective clustering across windows, we use a statistical measure that allows the comparison and merging of different clusters based on the calculation of the Wasserstein distance between histograms.