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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| Veröffentlicht in: | Computers & geosciences Jg. 110; S. 90 - 95 |
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| Abstract | 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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| AbstractList | 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. 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. |
| Author | Scitovski, Sanja |
| Author_xml | – sequence: 1 givenname: Sanja surname: Scitovski fullname: Scitovski, Sanja email: sscitov@unios.hr organization: University of Osijek, Trg Sv. Trojstva 3, HR-31 000 Osijek, Croatia |
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| Cites_doi | 10.1016/j.patrec.2011.06.003 10.1007/s11590-011-0389-9 10.1016/j.ins.2015.02.011 10.1007/s00521-010-0373-9 10.1016/j.procs.2013.05.200 10.1016/j.patrec.2009.08.008 10.1016/j.eswa.2011.01.135 10.17535/crorr.2014.0010 10.1080/19475705.2012.731659 10.1007/s10898-012-0020-3 10.1007/s10898-017-0510-4 10.1016/j.cageo.2013.06.010 10.1016/j.datak.2006.01.013 10.5194/npg-17-293-2010 10.3390/e17075000 10.1023/A:1026484815539 10.1016/j.cageo.2014.09.003 10.1016/j.eswa.2010.05.050 10.1111/j.1365-3121.1996.tb00728.x 10.1007/s12145-017-0295-5 |
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