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.
Graded Possibilistic Clustering of Non-stationary Data Streams / A.R.A. Abdullatif, F. Masulli, S. Rovetta, A. Cabri (LECTURE NOTES IN COMPUTER SCIENCE). - In: Fuzzy Logic and Soft Computing Applications / [a cura di] A. Petrosino, V. Loia, W. Pedrycz. - [s.l] : Springer, 2017. - ISBN 978-3-319-52961-5. - pp. 139-150 (( Intervento presentato al 11. convegno WILF tenutosi a Napoli nel 2016 [10.1007/978-3-319-52962-2_12].
Graded Possibilistic Clustering of Non-stationary Data Streams
A. Cabri
2017
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.
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