Abstract
Different motivation are related with the analysis of Spatial Big Data (SBD). Google Earth, Google Maps, Navigation, location-based service allow to obtain a great amount of geo-referenced data. Often spatial datasets exceed the capacity of current computing systems to manage, process, or analyze the data with reasonable effort. Considering SBD history methodology as Data-intensive Computing and Data Mining techniques have been useful. In this context the problem regards the analysis of of high frequency spatial data. In this paper we present an approach to clustering of high dimensional data which allows a flexible approach to the statistical modeling of phenomena characterized by unobserved heterogeneity. We consider the MDBSCAN and compare it with the classical k-means approach. The applications concern a synthetic data set and a data set of satellite images.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
References
Bailey, T.C., Gatrell, A.C.: Interactive Spatial Data Analysis. Addison Wesley Longman, Edinburgh (1996)
Cressie, N.A.C.: Statistics for spatial data. John Wiley & Sons, London (1993)
El-Sonbaty, Y., Ismail, M.A., Farouk, M.: An efficient density-based clustering algorithm for large databases In: Proceedings of the 16th IEEE International Conference on Tods with Artificial Intelligence (ICTAI) (2004)
Ester, M., Kriegel, H.P., Sander, J., Xiaowei, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceeding of the 2nd International Confererence on Knowledge Discovery and Data Mining, pp. 94–99 (1996)
Fayyad, U., Piatesky-Shapiro, G., Smyth, P.: From Data Mining to Knowledge Discovery in Databases (1996). http://www.kdnuggets.com/gpspubs/aimag-kdd-overview-1996-Fayyad.pdf
Han, J., Kamber, M., Tung, A.K.H.: Spatial Clutering Methods in Data Mining: A Survey (2001). ftp://fas.sfu.ca/pub/cs/han/pdf/gkdbk01.pdf
Jan, A.K.: Data Clustering. 50 years beyond K-means. Pattern Recognition Letters, 651-666 (2010)
Koperski, K., Han, J., Adhikary, J.: Mining Knowledge in Geographical Data (1998). ftp://fas.sfu.ca/pubcs/han/pdf/geo_survey98.pdf
Mao, J., Jain, A.K.: A self-organizing network for hyper-ellipsoidal clustering (HEC). IEEE Trans. Neural Networks, 16–29 (1996)
Sander, J., Ester, M., Kriegel, H.P., Xiaowei, X.: Density-Based Clustering. from in Spatial Databases: The Algorithm GDBSCAN and its applications (1999). http://www.dbs.informatik.uni-muenchen.de/Publikationen/
Schoier, G., Borruso, G.: A clustering method for large spatial databases. In: Laganá, A., Gavrilova, M.L., Kumar, V., Mun, Y., Tan, C., Gervasi, O. (eds.) ICCSA 2004. LNCS, vol. 3044, pp. 1089–1095. Springer, Heidelberg (2004)
Schoier, G., Bato, B.: A modification of the DBSCAN Algorithm in a Spatial Data Mining Approach. In: Meeting of the Classification and Data Analysis Group of the SIS: CLADAG 2007, pp. 395-398 . Macerata: EUM, Macerata, settembre, 12-14, 2007
Steinbach, M., Ertöz, L., Kumar, V.: The Challenges of Clustering High Dimensional Data (2003). http://www-users.cs.umn.edu/~kumar/papers/high_dim_clustering_19.pdf
Xu, R., Wunsch II., D.: Survey of Clustering Algorithms (2005). http://ieeexplore.ieee.org/iel5/72/30822/01427769.pdf
Bedard, Y.: Beyond GIS: Spatial On-Line Analytical Processing and Big Data, Univ. of Maine (2014). http://umaine.edu/scis/files//09/Beyond-GIS-Spatial-On-Line-Analytical-Processing-and-Big-Data-Yvan-Bedard.pdf
Chen, Y., Suel, T., Markowetz, A.: Efficient query processing in geographic web search engines. In: SIGMOD 2006, Chicago, Illinois, USA, June 27–29, 2006. http://cis.poly.edu/suel/papers/geoquery.pdf
Worboys, M., Duckham, M.: GIS.: A Computing Perspective. CRC Press, Boca Raton (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Schoier, G., Borruso, G. (2015). On the Problem of Clustering Spatial Big Data. In: Gervasi, O., et al. Computational Science and Its Applications -- ICCSA 2015. ICCSA 2015. Lecture Notes in Computer Science(), vol 9157. Springer, Cham. https://doi.org/10.1007/978-3-319-21470-2_50
Download citation
DOI: https://doi.org/10.1007/978-3-319-21470-2_50
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-21469-6
Online ISBN: 978-3-319-21470-2
eBook Packages: Computer ScienceComputer Science (R0)