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2008 Eighth IEEE International Conference on Data Mining
Clustering Geospatial Objects via Hidden Markov Random Fields
December 15-December 19
ISBN: 978-0-7695-3502-9
| ASCII Text | x | ||
| Makoto Sato, Shuuichiro Imahara, "Clustering Geospatial Objects via Hidden Markov Random Fields," Data Mining, IEEE International Conference on, pp. 1013-1018, 2008 Eighth IEEE International Conference on Data Mining, 2008. | |||
| BibTex | x | ||
| @article{ 10.1109/ICDM.2008.70, author = {Makoto Sato and Shuuichiro Imahara}, title = {Clustering Geospatial Objects via Hidden Markov Random Fields}, journal ={Data Mining, IEEE International Conference on}, volume = {0}, year = {2008}, issn = {1550-4786}, pages = {1013-1018}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2008.70}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - Data Mining, IEEE International Conference on TI - Clustering Geospatial Objects via Hidden Markov Random Fields SN - 1550-4786 SP1013 EP1018 A1 - Makoto Sato, A1 - Shuuichiro Imahara, PY - 2008 VL - 0 JA - Data Mining, IEEE International Conference on ER - | |||
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2008.70
This paper addresses the problem of clustering objects located and correlated geographically and containing multiple attributes. For the clustering problem, it is necessary to consider both the similarities of the attributes and the spatial dependencies of the objects. A new clustering framework using hidden Markov random fields (HMRFs) and Gaussian distributions and new potential models of HMRFs for irregularly located geospatial objects are proposed in this paper. Experimental results for systematic data and two real-world data showed the availability of the proposed algorithms.
Citation:
Makoto Sato, Shuuichiro Imahara, "Clustering Geospatial Objects via Hidden Markov Random Fields," icdm, pp.1013-1018, 2008 Eighth IEEE International Conference on Data Mining, 2008
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