DATA MINING METHODS FOR GIS ANALYSIS OF SEISMIC VULNERABILITY

Florin Leon, Gabriela M. Atanasiu

Abstract

This paper aims at designing some data mining methods of evaluating the seismic vulnerability of regions in the built infrastructure. A supervised clustering methodology is employed, based on k-nearest neighbor graphs. Unlike other classification algorithms, the method has the advantage of taking into account any distribution of training instances and also data topology. For the particular problem of seismic vulnerability analysis using a Geographic Information System, the gradual formation of clusters (for different values of k) allows a decision- making stakeholder to visualize more clearly the details of the cluster areas. The performance of the k-nearest neighbor graph method is tested on three classification problems, and finally it is applied to a sample from a digital map of Iaşi, a large city located in the North-Eastern part of Romania.

References

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Paper Citation


in Harvard Style

Leon F. and M. Atanasiu G. (2006). DATA MINING METHODS FOR GIS ANALYSIS OF SEISMIC VULNERABILITY . In Proceedings of the First International Conference on Software and Data Technologies - Volume 2: ICSOFT, ISBN 978-972-8865-69-6, pages 153-156. DOI: 10.5220/0001308301530156


in Bibtex Style

@conference{icsoft06,
author={Florin Leon and Gabriela M. Atanasiu},
title={DATA MINING METHODS FOR GIS ANALYSIS OF SEISMIC VULNERABILITY},
booktitle={Proceedings of the First International Conference on Software and Data Technologies - Volume 2: ICSOFT,},
year={2006},
pages={153-156},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001308301530156},
isbn={978-972-8865-69-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the First International Conference on Software and Data Technologies - Volume 2: ICSOFT,
TI - DATA MINING METHODS FOR GIS ANALYSIS OF SEISMIC VULNERABILITY
SN - 978-972-8865-69-6
AU - Leon F.
AU - M. Atanasiu G.
PY - 2006
SP - 153
EP - 156
DO - 10.5220/0001308301530156