Mitigating the effect of dataset shift in clustering

Sebastián Maldonado, Ramiro Saltos, Carla Vairetti*, José Delpiano

*Autor correspondiente de este trabajo

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

3 Citas (Scopus)


Dataset shift is a relevant topic in unsupervised learning since many applications face evolving environments, causing an important loss of generalization and performance. Most techniques that deal with this issue are designed for data stream clustering, whose goal is to process sequences of data efficiently under Big Data. In this study, we claim dataset shift is an issue for static clustering tasks in which data is collected over a long period. To mitigate it, we propose Time-weighted kernel k-means, a k-means variant that includes a time-dependent weighting process. We do this via the induced ordered weighted average (IOWA) operator. The weighting process acts as a gradual forgetting mechanism, prioritizing recent examples over outdated ones in the clustering algorithm. The computational experiments show the potential Time-weighted kernel k-means has in evolving environments.

Idioma originalInglés
Número de artículo109058
Páginas (desde-hasta)109058
PublicaciónPattern Recognition
EstadoPublicada - feb. 2023

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