Graded possibilistic clustering of non-stationary data streams

Description

Yes / 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.

Links & Downloads

1. 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.
2. http://hdl.handle.net/10454/17629
3. https://doi.org/10.1007/978-3-319-52962-2_12

Tags

Cluster model

Concept drift

Ambient assisted living

Annealing schedule

Concept shift

Additional Fields

Identifer	oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/17629
Date	27 January 2020
Creators	Abdullatif, Amr R.A., Masulli, F., Rovetta, S., Cabri, A.
Publisher	Springer, Cham
Source Sets	Bradford Scholars
Language	English
Detected Language	English
Type	Book chapter, Accepted manuscript
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.