Graded possibilistic clustering of non-stationary data streams

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Publication date
2017-02
Author
Abdullatif, Amr R.A.
Masulli, F.
Rovetta, S.
Cabri, A.
Keyword
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

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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 Version of Record
https://doi.org/10.1007/978-3-319-52962-2_12
Type
Book chapter
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