A density-based clustering algorithm for earthquake zoning

A possibility of applying the density-based clustering algorithm Rough-DBSCAN for earthquake zoning is considered in the paper. By using density-based clustering for earthquake zoning it is possible to recognize nonconvex shapes, what gives much more realistic results. Special attention is thereby p...

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Bibliographic Details
Published in:Computers & geosciences Vol. 110; pp. 90 - 95
Main Author: Scitovski, Sanja
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.01.2018
Subjects:
algorithms
Big data
Croatia
Density-based clustering
Earthquake zoning
earthquakes
Rough-DBSCAN
zoning
Croatia
Rough-DBSCAN
Big data
Earthquake zoning
Density-based clustering
ISSN:0098-3004, 1873-7803
Online Access:Get full text
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Summary:A possibility of applying the density-based clustering algorithm Rough-DBSCAN for earthquake zoning is considered in the paper. By using density-based clustering for earthquake zoning it is possible to recognize nonconvex shapes, what gives much more realistic results. Special attention is thereby paid to the problem of determining the corresponding value of the parameter ɛ in the algorithm. The size of the parameter ɛ significantly influences the recognizing number and configuration of earthquake zones. A method for selecting the parameter ɛ in the case of big data is also proposed. The method is applied to the problem of earthquake data zoning in a wider area of the Republic of Croatia. •Density-based clustering algorithm for earthquake zoning.•There is the possibility to recognize nonconvex shapes.•Defining of the parameter ɛ in the case of big data is proposed.
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ISSN:0098-3004
1873-7803
DOI:10.1016/j.cageo.2017.08.014