Soft Computing

, Volume 22, Issue 5, pp 1719–1730 | Cite as

Fuzzy extensions of the DBScan clustering algorithm

  • Dino Ienco
  • Gloria Bordogna
Methodologies and Application


The DBSCAN algorithm is a well-known density-based clustering approach particularly useful in spatial data mining for its ability to find objects’ groups with heterogeneous shapes and homogeneous local density distributions in the feature space. Furthermore, it can be suitable as scaling down approach to deal with big data for its ability to remove noise. Nevertheless, it suffers for some limitations, mainly the inability to identify clusters with variable density distributions and partially overlapping borders, which is often a characteristics of both scientific data and real-world data. To this end, in this work, we propose three fuzzy extensions of the \(\textit{DBSCAN}\) algorithm to generate clusters with distinct fuzzy density characteristics. The original version of \(\textit{DBSCAN}\) requires two precise parameters (minPts and \(\epsilon \)) to define locally dense areas which serve as seeds of the clusters. Nevertheless, precise values of both parameters may be not appropriate in all regions of the dataset. In the proposed extensions of \(\textit{DBSCAN}\), we define soft constraints to model approximate values of the input parameters. The first extension, named \(\textit{Fuzzy Core DBSCAN}\), relaxes the constraint on the neighbourhood’s density to generate clusters with fuzzy core points, i.e. cores with distinct density; the second extension, named \(\textit{Fuzzy Border DBSCAN}\), relaxes \(\epsilon \) to allow the generation of clusters with overlapping borders. Finally, the third extension, named \(\textit{Fuzzy DBSCAN}\) subsumes the previous ones, thus allowing to generate clusters with both fuzzy cores and fuzzy overlapping borders. Our proposals are compared w.r.t. state of the art fuzzy clustering methods over real-world datasets.


Fuzzy clustering Density-based clustering DBSCAN clustering 


Compliance with ethical standards

Conflict of interest

Dino Ienco and Gloria Bordogna declares that they have no conflict of interest.

Ethical standard

This article does not contain any studies with human participants or animals performed by any of the authors.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.IRSTEA, UMR TETISMontpellierFrance
  2. 2.LIRMMMontpellierFrance
  3. 3.CNR IREAMilanItaly

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