Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/26770

TitleDynamic analytics for spatial data with an incremental clustering approach
Author(s)Mendes, Fernando Alberto dos Santos
Santos, Maribel Yasmina
Pires, João Moura
KeywordsClustering
Incremental clustering
Shared nearest neighbor
Spatial data
Issue dateDec-2013
PublisherIEEE
JournalInternational Conference on Data Mining Workshops
Abstract(s)Several clustering algorithms have been extensively used to analyze vast amounts of spatial data. One of these algorithms is the SNN (Shared Nearest Neighbor), a densitybased algorithm, which has several advantages when analysing this type of data due to its ability of identifying clusters of different shapes, sizes and densities, as well as the capability to deal with noise. Having into account that data are usually progressively collected as time passes, incremental clustering approaches are required when there is the need to update the clustering results as new data become available. This paper proposes SNN++, an incremental clustering algorithm based on the SNN. Its performance and the quality of the resulting clusters are compared with the SNN and the results show that the SNN++ yields the same result as the SNN and show that the incremental feature was added to the SNN without any computational penalty. Moreover, the experimental results also show that processing huge amounts of data using increments considerably decreases the number of distances that need to be computed to identify the points’ nearest neighbors.
TypeConference paper
URIhttp://hdl.handle.net/1822/26770
DOI10.1109/ICDMW.2013.169
ISSN2375-9232
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:CAlg - Artigos em livros de atas/Papers in proceedings

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