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Clustering Earthquake Data: Identifying Spatial Patterns From Non-Spatial Attributes

Clustering Earthquake Data: Identifying Spatial Patterns From Non-Spatial Attributes

Cihan Savaş, Mehmet Samet Yıldız, Süleyman Eken, Cevat İkibaş, Ahmet Sayar
Copyright: © 2019 |Pages: 16
ISBN13: 9781522575191|ISBN10: 1522575197|ISBN13 Softcover: 9781522586180|EISBN13: 9781522575207
DOI: 10.4018/978-1-5225-7519-1.ch010
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MLA

Savaş, Cihan, et al. "Clustering Earthquake Data: Identifying Spatial Patterns From Non-Spatial Attributes." Big Data and Knowledge Sharing in Virtual Organizations, edited by Albert Gyamfi and Idongesit Williams, IGI Global, 2019, pp. 224-239. https://doi.org/10.4018/978-1-5225-7519-1.ch010

APA

Savaş, C., Yıldız, M. S., Eken, S., İkibaş, C., & Sayar, A. (2019). Clustering Earthquake Data: Identifying Spatial Patterns From Non-Spatial Attributes. In A. Gyamfi & I. Williams (Eds.), Big Data and Knowledge Sharing in Virtual Organizations (pp. 224-239). IGI Global. https://doi.org/10.4018/978-1-5225-7519-1.ch010

Chicago

Savaş, Cihan, et al. "Clustering Earthquake Data: Identifying Spatial Patterns From Non-Spatial Attributes." In Big Data and Knowledge Sharing in Virtual Organizations, edited by Albert Gyamfi and Idongesit Williams, 224-239. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-7519-1.ch010

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Abstract

Seismology, which is a sub-branch of geophysics, is one of the fields in which data mining methods can be effectively applied. In this chapter, employing data mining techniques on multivariate seismic data, decomposition of non-spatial variable is done. Then k-means clustering, density-based spatial clustering of applications with noise (DBSCAN), and hierarchical tree clustering algorithms are applied on decomposed data, and then pattern analysis is conducted using spatial data on the resulted clusters. The conducted analysis suggests that the clustering results with spatial data is compatible with the reality and characteristic features of regions related to earthquakes can be determined as a result of modeling seismic data using clustering algorithms. The baseline metric reported is clustering times for varying size of inputs.

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