Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/26768

Title4D+SNN: a spatio-temporal density-based clustering approach with 4D similarity
Author(s)Oliveira, João Ricardo Leite Mota
Santos, Maribel Yasmina
Pires, João Moura
KeywordsClustering
Density-based clustering
Spatio-temporal data
Distance function
Spatio-temporal clustering
Issue dateDec-2013
PublisherIEEE
JournalInternational Conference on Data Mining Workshops
Abstract(s)Spatio-temporal clustering is a subfield of data mining that is increasingly gaining more scientific attention due to the advances of location-based or environmental devices that register position, time and, in some cases, other semantic attributes. This process pretends to group objects based in their spatial and temporal similarity helping to discover interesting patterns and correlations in large data sets. One of the main challenges of this area is the ability to integrate several dimensions in a general-purpose approach. In this paper, such general approach is proposed, based on an extension of the SNN (Shared Nearest Neighbor) algorithm. The 4D+SNN algorithm allows the integration of space, time and one or more semantic attributes in the clustering process. This algorithm is able to deal with different data sets and different discovery purposes as the user has the ability to weight the importance of each dimension in the discovery process. The results obtained are very promising as show interesting findings on data and open the possibility of integration of several dimensions of analysis in the clustering process.
TypeConference paper
URIhttp://hdl.handle.net/1822/26768
DOI10.1109/ICDMW.2013.119
ISSN2375-9232
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:CAlg - Artigos em livros de atas/Papers in proceedings

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