Clustering of nonstationary data streams: A survey of fuzzy partitional methods. Abdullatif, A., Masulli, F., & Rovetta, S. WIREs Data Mining and Knowledge Discovery, 8(4):e1258, 2018. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/widm.1258![Clustering of nonstationary data streams: A survey of fuzzy partitional methods [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Data streams have arisen as a relevant research topic during the past decade. They are real-time, 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. This article is categorized under Technologies \textgreater Machine Learning Technologies \textgreater Computational Intelligence Fundamental Concepts of Data and Knowledge \textgreater Data Concepts
@article{abdullatif_clustering_2018,
title = {Clustering of nonstationary data streams: {A} survey of fuzzy partitional methods},
volume = {8},
issn = {1942-4795},
shorttitle = {Clustering of nonstationary data streams},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/widm.1258},
doi = {10.1002/widm.1258},
abstract = {Data streams have arisen as a relevant research topic during the past decade. They are real-time, 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. This article is categorized under Technologies {\textgreater} Machine Learning Technologies {\textgreater} Computational Intelligence Fundamental Concepts of Data and Knowledge {\textgreater} Data Concepts},
language = {en},
number = {4},
urldate = {2022-07-29},
journal = {WIREs Data Mining and Knowledge Discovery},
author = {Abdullatif, Amr and Masulli, Francesco and Rovetta, Stefano},
year = {2018},
note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/widm.1258},
keywords = {data streams, fuzzy clustering, nonstationary data, survey},
pages = {e1258},
}
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