@techreport{oai:ipsj.ixsq.nii.ac.jp:00169422,
 author = {Tatsuhiro, Sakai and Keiichi, Tamura and Kohei, Misaki and Hajime, Kitakami and Tatsuhiro, Sakai and Keiichi, Tamura and Kohei, Misaki and Hajime, Kitakami},
 issue = {4},
 month = {Jul},
 note = {Recently, the sizes and volumes of spatial databases have been increasing not only because of the popularity of geographical data, but also because of the popularity of geosocial media. A density-based spatial clustering algorithm is one of the simplest but most robust clustering techniques for geospatial data. Therefore, the speedup for the processing of density-based spatial clustering algorithms is one of the most important challenges. In this paper, we propose a new parallelization model using complex grid partitioning for density-based spatial clustering algorithm on a multi-core CPU. The main technique of the new parallelization model is that it forms complex spatial partition, n order to speed up the processing. The experimental results show that our new model outperforms a conventional data parallelization model., Recently, the sizes and volumes of spatial databases have been increasing not only because of the popularity of geographical data, but also because of the popularity of geosocial media. A density-based spatial clustering algorithm is one of the simplest but most robust clustering techniques for geospatial data. Therefore, the speedup for the processing of density-based spatial clustering algorithms is one of the most important challenges. In this paper, we propose a new parallelization model using complex grid partitioning for density-based spatial clustering algorithm on a multi-core CPU. The main technique of the new parallelization model is that it forms complex spatial partition, n order to speed up the processing. The experimental results show that our new model outperforms a conventional data parallelization model.},
 title = {A New Parallelization Model using Complex Grid Partitioning for Density-based Spatial Clustering Algorithm on a Multi-Core CPU},
 year = {2016}
}