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dc.contributor.advisor33
dc.contributor.authorMaldonado, Sebastián
dc.contributor.authorSaltos, Ramiro
dc.contributor.authorVairetti, Carla
dc.contributor.authorDelpiano, José
dc.coverageDOI: 10.1016/j.patcog.2022.109058
dc.date2023
dc.date.accessioned05-01-2026 18:04
dc.date.available05-01-2026 18:04
dc.date.issued33
dc.description.abstractDataset 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 The weighting process acts as a gradual forgetting mechanism, prioritizing recent examples over outdated ones in the clustering algorithm.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.
dc.identifierhttps://investigadores.uandes.cl/en/publications/97442da3-1a74-476a-8c78-b539169d1a85
dc.identifier.citation33
dc.identifier.uri33
dc.languageeng
dc.language.iso33
dc.publisher33
dc.relation33
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.sourcevol.134 (2023) p.109058
dc.subjectClustering
dc.subjectDataset shift
dc.subjectInduced ordered weighted average
dc.subjectKernel k-means
dc.subjectOWA operators
dc.title33
dc.titleMitigating the effect of dataset shift in clusteringeng
dc.title33spa
dc.title33und
dc.type33
dc.typeArticleeng
dc.typeArtículospa
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