Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/30162

TitleFast SNN-based clustering approach for large geospatial data sets
Author(s)Antunes, Arménio António Fernandes
Santos, Maribel Yasmina
Moreira, Adriano
KeywordsClustering
SNN
Nearest neighbours
Issue date2014
PublisherSpringer International Publishing
JournalLecture Notes in Geoinformation and Cartography
Abstract(s)Current positioning and sensing technologies enable the collection of very large spatio-temporal data sets. When analysing movement data, researchers often resort to clustering techniques to extract useful patterns from these data. Density- based clustering algorithms, although being very adequate to the analysis of this type of data, can be very inefficient when analysing huge amounts of data. The Shared Nearest Neighbour (SNN) algorithm presents low efficiency when dealing with high quantities of data due to its complexity evaluated in the worst case by O(n2). This chapter presents a clustering method, based on the SNN algorithm that significantly reduces the processing time by segmenting the spatial dimension of the data into a set of cells, and by minimizing the number of cells that have to be visited while searching for the k-nearest neighbours of each vector. The obtained results show an expressive reduction of the time needed to find the k-nearest neighbours and to compute the clusters, while producing results that are equal to those produced by the original SNN algorithm. Experimental results obtained with three different data sets (2D and 3D), one synthetic and two real, show that the proposed method enables the analysis of much larger data sets within reasonable amount of time.
TypeConference paper
DescriptionPublicado em "Connecting a digital Europe through location and place", Series title : Lecture notes in geoinformation and cartography
URIhttp://hdl.handle.net/1822/30162
ISBN978-3-319-03610-6
DOI10.1007/978-3-319-03611-3_11
ISSN1863-2246
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:CAlg - Artigos em livros de atas/Papers in proceedings

Files in This Item:
File Description SizeFormat 
AGILE2014_AM_MYS_AM.pdf
  Restricted access
Documento Principal491,41 kBAdobe PDFView/Open

Partilhe no FacebookPartilhe no TwitterPartilhe no DeliciousPartilhe no LinkedInPartilhe no DiggAdicionar ao Google BookmarksPartilhe no MySpacePartilhe no Orkut
Exporte no formato BibTex mendeley Exporte no formato Endnote Adicione ao seu ORCID