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.

A Fuzzy Density-based Clustering Algorithm for Streaming Data

Bechini A.;Marcelloni F.
;
Renda A.
2019-01-01

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.
2019
978-1-5386-1728-1
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1013325
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