Fuzzy extensions of the DBScan clustering algorithm
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 approa...
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| Vydáno v: | Soft computing (Berlin, Germany) Ročník 22; číslo 5; s. 1719 - 1730 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.03.2018
Springer Nature B.V Springer Verlag |
| Edice: | Methodologies and Application |
| Témata: | |
| ISSN: | 1432-7643, 1433-7479 |
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| Abstract | 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
DBSCAN
algorithm to generate clusters with distinct fuzzy density characteristics. The original version of
DBSCAN
requires two precise parameters (
minPts
and
ϵ
) 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
DBSCAN
, we define soft constraints to model approximate values of the input parameters. The first extension, named
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
Fuzzy Border
DBSCAN
, relaxes
ϵ
to allow the generation of clusters with overlapping borders. Finally, the third extension, named
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. |
|---|---|
| AbstractList | 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
DBSCAN
algorithm to generate clusters with distinct fuzzy density characteristics. The original version of
DBSCAN
requires two precise parameters (
minPts
and
ϵ
) 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
DBSCAN
, we define soft constraints to model approximate values of the input parameters. The first extension, named
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
Fuzzy Border
DBSCAN
, relaxes
ϵ
to allow the generation of clusters with overlapping borders. Finally, the third extension, named
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. 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 DBSCANDBSCAN algorithm to generate clusters with distinct fuzzy density characteristics. The original version of DBSCANDBSCAN requires two precise parameters (minPts and ϵϵ ) 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 DBSCANDBSCAN , we define soft constraints to model approximate values of the input parameters. The first extension, named Fuzzy Core DBSCANFuzzy 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 Fuzzy Border DBSCANFuzzy Border DBSCAN , relaxes ϵϵ to allow the generation of clusters with overlapping borders. Finally, the third extension, named Fuzzy DBSCANFuzzy 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. 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 DBSCAN algorithm to generate clusters with distinct fuzzy density characteristics. The original version of DBSCAN requires two precise parameters (minPts and ϵ) 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 DBSCAN, we define soft constraints to model approximate values of the input parameters. The first extension, named Fuzzy CoreDBSCAN, 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 Fuzzy BorderDBSCAN, relaxes ϵ to allow the generation of clusters with overlapping borders. Finally, the third extension, named 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. |
| Author | Ienco, Dino Bordogna, Gloria |
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| Cites_doi | 10.1016/0098-3004(84)90020-7 10.1016/j.fss.2013.12.011 10.1016/j.neucom.2006.06.017 10.1016/j.fss.2009.06.012 10.1109/TFUZZ.2004.840099 10.1023/A:1009745219419 10.1016/j.measurement.2014.04.034 10.1109/21.299710 10.3233/IFS-2012-0489 10.1109/FUZZY.2010.5584527 10.1007/978-3-319-08852-5_11 10.1145/1081870.1081955 |
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| Copyright | Springer-Verlag Berlin Heidelberg 2016 Springer-Verlag Berlin Heidelberg 2016. Distributed under a Creative Commons Attribution 4.0 International License |
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| Keywords | Density-based clustering DBSCAN clustering Fuzzy clustering |
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| SubjectTerms | Algorithms Artificial Intelligence Big Data Borders Clustering Computational Intelligence Computer Science Constraint modelling Control Data mining Datasets Density Engineering Fuzzy sets Information Retrieval Machine Learning Mathematical Logic and Foundations Mechatronics Methodologies and Application Neighborhoods Parameters Robotics Social networks Spatial data |
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| Title | Fuzzy extensions of the DBScan clustering algorithm |
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| Volume | 22 |
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