STING: A Statistical Information Grid Approach to Spatial Data Mining.

Wei Wang, Jiong Yang, Richard R. Muntz: STING: A Statistical Information Grid Approach to Spatial Data Mining. VLDB 1997: 186-195
  author    = {Wei Wang and
               Jiong Yang and
               Richard R. Muntz},
  editor    = {Matthias Jarke and
               Michael J. Carey and
               Klaus R. Dittrich and
               Frederick H. Lochovsky and
               Pericles Loucopoulos and
               Manfred A. Jeusfeld},
  title     = {STING: A Statistical Information Grid Approach to Spatial Data
  booktitle = {VLDB'97, Proceedings of 23rd International Conference on Very
               Large Data Bases, August 25-29, 1997, Athens, Greece},
  publisher = {Morgan Kaufmann},
  year      = {1997},
  isbn      = {1-55860-470-7},
  pages     = {186-195},
  ee        = {db/conf/vldb/WangYM97.html},
  crossref  = {DBLP:conf/vldb/97},
  bibsource = {DBLP,}


Spatial data mining, i.e., discovery of interesting characteristics and patterns that may implicitly exist in spatial databases, is a challenging task due to the huge amounts of spatial data and to the new conceptual nature of the problems which must account for spatial distance. Clustering and region oriented queries are common problems in this domain. Several approaches have been presented in recent years, all of which require at least one scan of all individual objects (points). Consequently, the computational complexity is at least linearly proportional to the number of objects to answer each query. In this paper, we propose a hierarchical statistical information grid based approach for spatial data mining to reduce the cost further. The idea is to capture statistical information associated with spatial cells in such a manner that whole classes of queries and clustering problems can be answered without recourse to the individual objects. In theory, and confirmed by empirical studies, this approach outperforms the best previous method by at least an order of magnitude, especially when the data set is very large.

Copyright © 1997 by the VLDB Endowment. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by the permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee and/or special permission from the Endowment.

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Printed Edition

Matthias Jarke, Michael J. Carey, Klaus R. Dittrich, Frederick H. Lochovsky, Pericles Loucopoulos, Manfred A. Jeusfeld (Eds.): VLDB'97, Proceedings of 23rd International Conference on Very Large Data Bases, August 25-29, 1997, Athens, Greece. Morgan Kaufmann 1997, ISBN 1-55860-470-7
Contents BibTeX

Electronic Edition

From CS Dept., University Trier (Germany)


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Referenced by

  1. Anthony K. H. Tung, Raymond T. Ng, Laks V. S. Lakshmanan, Jiawei Han: Constraint-based clustering in large databases. ICDT 2001: 405-419
  2. Gholamhosein Sheikholeslami, Surojit Chatterjee, Aidong Zhang: WaveCluster: A Wavelet Based Clustering Approach for Spatial Data in Very Large Databases. VLDB J. 8(3-4): 289-304(2000)
  3. Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng, Jörg Sander: LOF: Identifying Density-Based Local Outliers. SIGMOD Conference 2000: 93-104
  4. Alexander Hinneburg, Daniel A. Keim: Optimal Grid-Clustering: Towards Breaking the Curse of Dimensionality in High-Dimensional Clustering. VLDB 1999: 506-517
  5. Wei Wang, Jiong Yang, Richard R. Muntz: STING+: An Approach to Active Spatial Data Mining. ICDE 1999: 116-125
  6. Pedro Furtado, Henrique Madeira: Summary Grids: Building Accurate Multidimensional Histograms. DASFAA 1999: 187-194
  7. Gholamhosein Sheikholeslami, Surojit Chatterjee, Aidong Zhang: WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases. VLDB 1998: 428-439
  8. Zuotao Li, Xiaoyang Sean Wang, Menas Kafatos, Ruixin Yang: A Pyramid Data Model for Supporting Content-Based Browsing and Knowledge Discovery. SSDBM 1998: 170-179
  9. Daniel Barbará, William DuMouchel, Christos Faloutsos, Peter J. Haas, Joseph M. Hellerstein, Yannis E. Ioannidis, H. V. Jagadish, Theodore Johnson, Raymond T. Ng, Viswanath Poosala, Kenneth A. Ross, Kenneth C. Sevcik: The New Jersey Data Reduction Report. IEEE Data Eng. Bull. 20(4): 3-45(1997)
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