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.
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. |
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