Graded possibilistic clustering of non-stationary data streams

Abdullatif, Amr R.A.; Masulli, F.; Rovetta, S.; Cabri, A.

dc.contributor.author	Abdullatif, Amr R.A.
dc.contributor.author	Masulli, F.
dc.contributor.author	Rovetta, S.
dc.contributor.author	Cabri, A.
dc.date.accessioned	2020-01-27T11:40:13Z
dc.date.accessioned	2020-02-12T15:51:12Z
dc.date.available	2020-01-27T11:40:13Z
dc.date.available	2020-02-12T15:51:12Z
dc.date.issued	2017-02
dc.identifier.citation	Abdullatif 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.uri	http://hdl.handle.net/10454/17629
dc.description	Yes	en_US
dc.description.abstract	Multidimensional 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.iso	en	en_US
dc.publisher	Springer, Cham
dc.relation.isreferencedby	https://doi.org/10.1007/978-3-319-52962-2_12	en_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.subject	Cluster model	en_US
dc.subject	Concept drift	en_US
dc.subject	Ambient assisted living	en_US
dc.subject	Annealing schedule	en_US
dc.subject	Concept shift	en_US
dc.title	Graded possibilistic clustering of non-stationary data streams	en_US
dc.status.refereed	Yes	en_US
dc.date.application	2017-02-07
dc.type	Book chapter	en_US
dc.type.version	Accepted manuscript	en_US
dc.date.updated	2020-01-27T11:40:19Z
refterms.dateFOA	2020-02-12T15:51:36Z