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4D-SNN: A Spatio-temporal Density-based Clustering Approach with 4D Similarity
December 2013
Oliveira13
Spatio-temporal clustering is a subfield of data mining that is increasingly gaining more scientific. 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.
In proceedings
Ricardo Oliveira, Maribel Yasmina Santos, João Moura Pires
International Workshop on Spatial and Spatiotemporal Data Mining
The IEEE International Conference on Data Mining (ICDM’2013)
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Ricardo Oliveira and Maribel Yasmina Santos and João Moura Pires, 4D-SNN: A Spatio-temporal Density-based Clustering Approach with 4D Similarity, , International Workshop on Spatial and Spatiotemporal Data Mining, The IEEE International Conference on Data Mining (ICDM’2013), December 2013.
Ricardo Oliveira, Maribel Yasmina Santos and <a href="/people/members/view.php?code=542b14e1830dcf7566974fd36b6fccc7" class="author">João Moura Pires</a>, <b>4D-SNN: A Spatio-temporal Density-based Clustering Approach with 4D Similarity</b>, <u>International Workshop on Spatial and Spatiotemporal Data Mining</u>, The IEEE International Conference on Data Mining (ICDM’2013), December 2013.
@inproceedings {Oliveira13, author = {Ricardo Oliveira and Maribel Yasmina Santos and Jo{\~a}o Moura Pires}, title = {4D-SNN: A Spatio-temporal Density-based Clustering Approach with 4D Similarity}, booktitle = {International Workshop on Spatial and Spatiotemporal Data Mining}, series = {The IEEE International Conference on Data Mining (ICDM’2013)}, abstract = {Spatio-temporal clustering is a subfield of data mining that is increasingly gaining more scientific. 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.}, keywords = {clustering; density-based clustering; spatio- temporal data; distance function; spatio-temporal clustering}, month = {December}, year = {2013}, }
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