@inproceedings{958d4f058a764691a3cf0afcdb40ceab,
title = "Geo-Spatial Analytics using the Dynamic ST-SNN Approach",
abstract = "Spatio-temporal clustering is a subfield of data mining that is increasingly gaining more scientific attention due to the advances of location-based devices that register position, time and, in some cases, other attributes. Spatio-temporal clustering intends to group objects based in their spatial and temporal similarity helping to discover interesting spatio-temporal patterns and correlations in large data sets. One of the main challenges of this area is the ability to integrate spatial, temporal and other numerical or classification information in a general-purpose approach as well as the capability to integrate, in the previously obtained clusters, newly available data. This paper presents the Dynamic ST-SNN approach in which the user has the possibility to simultaneously analyse several dimensions and incrementally add new-collected data to the existing clusters providing updated clusters.",
keywords = "Spatial Data, Spatio-Temporal Data, Clustering, Density-based Clustering, SNN, DIFFERENT SIZES, DENSITIES, ALGORITHM, CLUSTERS, SHAPES",
author = "Santos, {Maribel Yasmina} and Pires, {Joao Moura} and Guilherme Moreira and Ricardo Oliveira and Fernando Mendes and Carlos Costa",
note = "sem pdf; World Congress on Engineering (WCE 2015) ; Conference date: 01-07-2015 Through 03-07-2015",
year = "2015",
language = "English",
isbn = "978-988-19253-4-3",
series = "Lecture Notes in Engineering and Computer Science",
publisher = "INT ASSOC ENGINEERS-IAENG",
pages = "285--290",
editor = "SI Ao and L Gelman and DWL Hukins and A Hunter and AM Korsunsky",
booktitle = "WORLD CONGRESS ON ENGINEERING, WCE 2015, VOL I",
}