ACM SIGMOD Anthology ACM SIGMOD dblp.uni-trier.de

GeoMiner: A System Prototype for Spatial Data Mining.

Jiawei Han, Krzysztof Koperski, Nebojsa Stefanovic: GeoMiner: A System Prototype for Spatial Data Mining. SIGMOD Conference 1997: 553-556
@inproceedings{DBLP:conf/sigmod/HanKS97,
  author    = {Jiawei Han and
               Krzysztof Koperski and
               Nebojsa Stefanovic},
  editor    = {Joan Peckham},
  title     = {GeoMiner: A System Prototype for Spatial Data Mining},
  booktitle = {SIGMOD 1997, Proceedings ACM SIGMOD International Conference
               on Management of Data, May 13-15, 1997, Tucson, Arizona, USA},
  publisher = {ACM Press},
  year      = {1997},
  pages     = {553-556},
  ee        = {http://doi.acm.org/10.1145/253260.253404, db/conf/sigmod/HanKS97.html},
  crossref  = {DBLP:conf/sigmod/97},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}
BibTeX

Abstract

Spatial data mining is to mine high-level spatial information and knowledge from large spatial databases. A spatial data mining system prototype, GeoMiner, has been designed and developed based on our years of experience in the research and development of relational data mining system, DBMiner, and our research into spatial data mining. The data mining power of GeoMiner includes mining three kinds of rules: characteristic rules, comparison rules, and association rules, in gee-spatial databases, with a planned extension to include mining classification rules and clustering rules. The SAND (Spatial And Nonspatial Data) architecture is applied in the modeling of spatial databases, whereas GeoMiner includes the spatial data cube construction module, spatial on-line analytical processing (OLAP) module, and spatial data mining modules. A spatial data mining language, GMQL (Geo-Mining Query Language), is designed and implemented as an extension to Spatial SQL [3], for spatial data mining. Moreover, an interactive, user-friendly data mining interface is constructed and tools are implemented for visualization of discovered spatial knowledge.

Copyright © 1997 by the ACM, Inc., used by permission. Permission to make digital or hard copies is granted provided that copies are not made or distributed for profit or direct commercial advantage, and that copies show this notice on the first page or initial screen of a display along with the full citation.


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Joan Peckham (Ed.): SIGMOD 1997, Proceedings ACM SIGMOD International Conference on Management of Data, May 13-15, 1997, Tucson, Arizona, USA. ACM Press 1997 BibTeX , SIGMOD Record 26(2), June 1997
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References

[1]
Rakesh Agrawal, Ramakrishnan Srikant: Fast Algorithms for Mining Association Rules in Large Databases. VLDB 1994: 487-499 BibTeX
[2]
Ming-Syan Chen, Jiawei Han, Philip S. Yu: Data Mining: An Overview from a Database Perspective. IEEE Trans. Knowl. Data Eng. 8(6): 866-883(1996) BibTeX
[3]
Max J. Egenhofer: Spatial SQL: A Query and Presentation Language. IEEE Trans. Knowl. Data Eng. 6(1): 86-95(1994) BibTeX
[4]
Martin Ester, Hans-Peter Kriegel, Jörg Sander, Xiaowei Xu: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. KDD 1996: 226-231 BibTeX
[5]
Usama M. Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth, Ramasamy Uthurusamy (Eds.): Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press 1996, ISBN 0-262-56097-6
Contents BibTeX
[6]
Ralf Hartmut Güting: An Introduction to Spatial Database Systems. VLDB J. 3(4): 357-399(1994) BibTeX
[7]
Jiawei Han, Yandong Cai, Nick Cercone: Data-Driven Discovery of Quantitative Rules in Relational Databases. IEEE Trans. Knowl. Data Eng. 5(1): 29-40(1993) BibTeX
[8]
Jiawei Han, Yongjian Fu: Discovery of Multiple-Level Association Rules from Large Databases. VLDB 1995: 420-431 BibTeX
[9]
Jiawei Han, Yongjian Fu, Wei Wang, Jenny Chiang, Wan Gong, Krzysztof Koperski, Deyi Li, Yijun Lu, Amynmohamed Rajan, Nebojsa Stefanovic, Betty Xia, Osmar R. Zaïane: DBMiner: A System for Mining Knowledge in Large Relational Databases. KDD 1996: 250-255 BibTeX
[10]
Krzysztof Koperski, Jiawei Han: Discovery of Spatial Association Rules in Geographic Information Databases. SSD 1995: 47-66 BibTeX
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[12]
...
[13]
Raymond T. Ng, Jiawei Han: Efficient and Effective Clustering Methods for Spatial Data Mining. VLDB 1994: 144-155 BibTeX
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Tian Zhang, Raghu Ramakrishnan, Miron Livny: BIRCH: An Efficient Data Clustering Method for Very Large Databases. SIGMOD Conference 1996: 103-114 BibTeX

Referenced by

  1. Wei Wang, Jiong Yang, Richard R. Muntz: STING+: An Approach to Active Spatial Data Mining. ICDE 1999: 116-125
BibTeX
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