Thumbnail Image
Publication

Graded possibilistic clustering of non-stationary data streams

Abdullatif, Amr R.A.
; 
Masulli, F.
; 
Rovetta, S.
; 
Cabri, A.
Publication Date
2017-02
End of Embargo
Supervisor
Keywords
Cluster model, Concept drift, Ambient assisted living, Annealing schedule, Concept shift
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.
Peer-Reviewed
Yes
Open Access status
Accepted for publication
Institution
Department
Awarded
Embargo end date
Collections
Engineering and Digital Technology Publications
Show all metadata
Files
Thumbnail Image
Abdullatif_book_chapter
Adobe PDF, 1.32 MB
Additional title
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.
URI
http://hdl.handle.net/10454/17629
Version
Accepted manuscript
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.
Link to publisher’s version
Link to published version
Link to Version of Record
https://doi.org/10.1007/978-3-319-52962-2_12
Type
Book chapter
Qualification name
Notes