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dc.contributor.authorAbdullatif, Amr R.A.
dc.contributor.authorMasulli, F.
dc.contributor.authorRovetta, S.
dc.contributor.authorCabri, A.
dc.date.accessioned2020-01-27T11:40:13Z
dc.date.accessioned2020-02-12T15:51:12Z
dc.date.available2020-01-27T11:40:13Z
dc.date.available2020-02-12T15:51:12Z
dc.date.issued2017-02
dc.identifier.citationAbdullatif A, Masulli F, Rovetta S et al (2017) Graded possibilisitic clustering of non-stationary data streams. In: Petrosino A, Loia V and Pedrycz E (Eds.) Fuzzy logic and soft computing applications. WILF 2016. Lecture notes in computer science: 10147. Springer.en_US
dc.identifier.urihttp://hdl.handle.net/10454/17629
dc.descriptionYesen_US
dc.description.abstractMultidimensional data streams are a major paradigm in data science. This work focuses on possibilistic clustering algorithms as means to perform clustering of multidimensional streaming data. The proposed approach exploits fuzzy outlier analysis to provide good learning and tracking abilities in both concept shift and concept drift.en_US
dc.language.isoenen_US
dc.publisherSpringer, Cham
dc.relation.isreferencedbyhttps://doi.org/10.1007/978-3-319-52962-2_12en_US
dc.rights©Springer International Publishing AG 2017. Reproduced in accordance with the publisher's self-archiving policy. The final authenticated version is available online at https://doi.org/10.1007/978-3-319-52962-2_12.en_US
dc.subjectCluster modelen_US
dc.subjectConcept driften_US
dc.subjectAmbient assisted livingen_US
dc.subjectAnnealing scheduleen_US
dc.subjectConcept shiften_US
dc.titleGraded possibilistic clustering of non-stationary data streamsen_US
dc.status.refereedYesen_US
dc.date.application2017-02-07
dc.typeBook chapteren_US
dc.type.versionAccepted manuscripten_US
dc.date.updated2020-01-27T11:40:19Z
refterms.dateFOA2020-02-12T15:51:36Z


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