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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Veröffentlicht in:	Soft computing (Berlin, Germany) Jg. 22; H. 5; S. 1719 - 1730
Hauptverfasser:	Ienco, Dino, Bordogna, Gloria
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2018 Springer Nature B.V Springer Verlag
Schriftenreihe:	Methodologies and Application
Schlagworte:	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 Density-based clustering DBSCAN clustering Fuzzy clustering
ISSN:	1432-7643, 1433-7479
Online-Zugang:	Volltext
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