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Clustering of nonstationary data streams: a survey of fuzzy partitional methods
Abdullatif, Amr R.A. Masulli, F. Rovetta, S.
Abdullatif, Amr R.A.
Masulli, F.
Rovetta, S.
Publication Date
2018-07
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Ā© 2018 Wiley
This is the peer reviewed version of the following article: Abdullatif A, Masulli F and Rovetta S (2018) Clustering of nonstationary data streams: a survey of fuzzy partitional methods. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 8(4): e1258, which has been published in final form at https://doi.org/10.1002/widm.1258. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.
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2018-03-18
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Abstract
Data streams have arisen as a relevant research topic during the past decade. They are realātime, incremental in nature, temporally ordered, massive, contain outliers, and the objects in a data stream may evolve over time (concept drift). Clustering is often one of the earliest and most important steps in the streaming data analysis workflow. A comprehensive literature is available about stream data clustering; however, less attention is devoted to the fuzzy clustering approach, even though the nonstationary nature of many data streams makes it especially appealing. This survey discusses relevant data stream clustering algorithms focusing mainly on fuzzy methods, including their treatment of outliers and concept drift and shift.
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Abdullatif A, Masulli F and Rovetta S (2018) Clustering of nonstationary data streams: a survey of fuzzy partitional methods. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 8(4): e1258.
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