Title :
Zoning by k-Means over a Large Data Set
Author :
Martinez, Carlos ; Lozano, Jose ; de la Fuente, D. ; Priore, P. ; Garcia, Narciso
Author_Institution :
Univ. of Oviedo, Gijon, Spain
Abstract :
In this paper, for zoning a large set of location´s data we apply the k-means clustering algorithm. The results were plotted graphically and were satisfactory, so we conclude that the algorithm is useful despite the size of the data, at least for low data dimensions (latitude, longitude).
Keywords :
data handling; geographic information systems; pattern clustering; k-means clustering algorithm; large data set; location data; low data dimensions; zoning; Abstracts; Algorithm design and analysis; Australia; Cities and towns; Clustering algorithms; Clustering methods; XML; k-means; zoning;
Conference_Titel :
Artificial Intelligence (MICAI), 2013 12th Mexican International Conference on
Conference_Location :
Mexico City
Print_ISBN :
978-1-4799-2604-6
DOI :
10.1109/MICAI.2013.13
