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.
CITATION STYLE
Abdullatif, A., Masulli, F., Rovetta, S., & Cabri, A. (2017). Graded possibilistic clustering of non-stationary data streams. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10147 LNAI, 139–150. https://doi.org/10.1007/978-3-319-52962-2_12
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