@inproceedings{aliperti_fuzzy_2019,
	title = {A {Fuzzy} {Density}-based {Clustering} {Algorithm} for {Streaming} {Data}},
	doi = {10.1109/FUZZ-IEEE.2019.8858909},
	abstract = {The exploitation of data streams, nowadays provided nonstop by a myriad of diverse applications, asks for specific analysis methods. In this paper, we propose SF-DBSCAN, a fuzzy version of the DBSCAN algorithm, aimed to perform unsupervised analysis of streaming data. Fuzziness is introduced by fuzzy borders of density-based clusters. We describe and discuss the proposed algorithm, which evolves the clusters at each occurrence of a new object. Three synthetic datasets are used to show the ability of SF-DBSCAN to successfully track changes of data distribution, thus properly addressing concept drift. SF-DBSCAN is compared with a basic, crisp streaming version of DBSCAN with regard to modelling effectiveness.},
	booktitle = {2019 {IEEE} {International} {Conference} on {Fuzzy} {Systems} ({FUZZ}-{IEEE})},
	author = {Aliperti, Andrea and Bechini, Alessio and Marcelloni, Francesco and Renda, Alessandro},
	month = jun,
	year = {2019},
	note = {ISSN: 1558-4739},
	keywords = {Clustering algorithms, Data structures, Memory management, Partitioning algorithms, Proposals, Sensitivity, Shape},
	pages = {1--6},
}

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