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Dynamic Analytics for Spatial Data with an Incremental Clustering Approach
December 2013
Mendes13
SNN (Shared Nearest Neighbor), a density-based clustering algorithm, has several advantages for spatial 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.
In proceedings
Fernando Mendes, Maribel Yasmina Santos, João Moura Pires
Incremental clustering, concept drift and novelty detection workshop
The IEEE International Conference on Data Mining (ICDM’2013)
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Fernando Mendes and Maribel Yasmina Santos and João Moura Pires, Dynamic Analytics for Spatial Data with an Incremental Clustering Approach, , Incremental clustering, concept drift and novelty detection workshop, The IEEE International Conference on Data Mining (ICDM’2013), December 2013.
Fernando Mendes, Maribel Yasmina Santos and <a href="/people/members/view.php?code=542b14e1830dcf7566974fd36b6fccc7" class="author">João Moura Pires</a>, <b>Dynamic Analytics for Spatial Data with an Incremental Clustering Approach</b>, <u>Incremental clustering, concept drift and novelty detection workshop</u>, The IEEE International Conference on Data Mining (ICDM’2013), December 2013.
@inproceedings {Mendes13, author = {Fernando Mendes and Maribel Yasmina Santos and Jo{\~a}o Moura Pires}, title = {Dynamic Analytics for Spatial Data with an Incremental Clustering Approach}, booktitle = {Incremental clustering, concept drift and novelty detection workshop}, series = {The IEEE International Conference on Data Mining (ICDM’2013)}, abstract = {SNN (Shared Nearest Neighbor), a density-based clustering algorithm, has several advantages for spatial 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.}, keywords = {clustering; incremental clustering; shared nearest neighbor; spatial data.}, month = {December}, year = {2013}, }
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