Authors:
Florin Leon
1
and
Gabriela M. Atanasiu
2
Affiliations:
1
Faculty of Automatic Control and Computer Science, ”Gh. Asachi” Technical University, Romania
;
2
Faculty of Civil Engineering, ”Gh. Asachi” Technical University, Romania
Keyword(s):
Data mining, Geographic Information Systems, Supervised clustering, k-Nearest Neighbor, Seismic risk management.
Related
Ontology
Subjects/Areas/Topics:
Business Analytics
;
Communication and Software Technologies and Architectures
;
Data Engineering
;
Data Warehouses and Data Mining
;
e-Business
;
Energy and Economy
;
Energy-Aware Systems and Technologies
;
Enterprise Information Systems
;
Geographic Information Systems (GIS)
;
Information Systems Analysis and Specification
;
Management Information Systems
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.