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Clustering Algorithm for Spatial Data Mining: An Overview

by A. Padmapriya, N. Subitha
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 68 - Number 10
Year of Publication: 2013
Authors: A. Padmapriya, N. Subitha
10.5120/11617-7014

A. Padmapriya, N. Subitha . Clustering Algorithm for Spatial Data Mining: An Overview. International Journal of Computer Applications. 68, 10 ( April 2013), 28-33. DOI=10.5120/11617-7014

@article{ 10.5120/11617-7014,
author = { A. Padmapriya, N. Subitha },
title = { Clustering Algorithm for Spatial Data Mining: An Overview },
journal = { International Journal of Computer Applications },
issue_date = { April 2013 },
volume = { 68 },
number = { 10 },
month = { April },
year = { 2013 },
issn = { 0975-8887 },
pages = { 28-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume68/number10/11617-7014/ },
doi = { 10.5120/11617-7014 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:27:29.389012+05:30
%A A. Padmapriya
%A N. Subitha
%T Clustering Algorithm for Spatial Data Mining: An Overview
%J International Journal of Computer Applications
%@ 0975-8887
%V 68
%N 10
%P 28-33
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Spatial data mining practice for the extraction of useful information and knowledge from massive and complex spatial database. Most research in this area has focused on efficient clustering algorithm for spatial database to analyze the complexity. This paper introduces an active spatial data mining approach that extends the current spatial data mining algorithms to efficiently support user-defined triggers on dynamically evolving spatial data. It shows that spatial data mining is a promising field, with fruitful research results and many challenging issues.

References
  1. R. Agrawal, M. Mehta, J. Shafer, R. Srikant, A. Arning, T. Bollinger. The Quest Data Mining System. Proceedings of 1996 International Conference on Data Mining and Knowledge Discovery(KDD'96), Portland, Oregon, pp. 244-249, August 1996.
  2. K. Alsabti, S. Ranka, and V. Singh, ªAn Efficient k-means Clustering Algorithm,º Proc. First Workshop High Performance Data Mining, Mar. 1998
  3. P. S. Bradley, U. Fayyad, and C. Reina, "Scaling Clustering Algorithms to Large Databases", Proc. 4 th International Conf. on Knowledge Discovery and Data Mining (KDD-98). AAAI Press, Aug. 1998
  4. M. S. Chen, J. Han, and P. S. Yu. Data Mining: An Overwiew from a Database Perspective. IEEE Transcations on Knowledge and Data Engineering, 8(6):883, 1996.
  5. Dan Pelleg and Andrew W. Moore. Accelerating exact k-means algorithms with geometric reasoning. In KDD, pages 277–281, 1999.
  6. Ester M. , Kriegel H. -P. , and Sander J. 1997 "Spatial Data Mining: A Database Approach", Proc. 5th Int. Symp. on Large Spatial Databases, Berlin, Germany, pp. 47-66.
  7. U. M. Fayyades, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy (Eds). Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press, 1996.
  8. W. Lu, J. Han, and B. C. Obi. Discovery of General Knowledge in Large Spatial Databases. In Proc. Far East Workshop on Geographic Information Systems pp. 275-289, Singapore, June 1993
  9. G. Karypis, E. -H. Han, and V. Kumar, "CHAMELEON: A Hierarchical Clustering Algorithm Using Dynamic Modeling," Computer, vol. 32, no. 8, pp 68–75, Aug. 1999
  10. L. Kaufman and P. J. Rousseeuw, Finding Groups in Data: an Introduction to Cluster Analysis. John Wiley & Sons, 1990.
  11. Koperski K. Adhikary J. , Han J. 1996 "Knowledge Discovery in Spatial Databases: Progress and Challenges", Proc. SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, Technical Report 96-08, University of British Columbia, Vancouver,Canada.
  12. K. Koperski and J. Han. Discovery of Spatial Association Rules in Geographic Information Databases. In Proc. th Int'l Symp. On Large Spatial Databases (SSD '95), pp. 47 66, Portland, Maine, August 1995
  13. Krzysztof Koperski, Junas Adhikary, JiaweiHan. Spatial Data Mining: Progress and Challenges Survey Paper. Workshop on Research Issues on Data Mining and Knowledge Discovery, 1996
  14. G. Milligan and M. Cooper, "An Examination of Procedures for Determining the Number of Clusters in a Data Set,"Psychometrika, vol. 50, pp. 159–179, 1985
  15. Paul S. Bradley and Usama M. Fayyad. Refining initial points for k-means clustering. In Jude W. Shavlik, editor, ICML, pages 91–99. Morgan Kaufmann, 1998.
  16. Raymond T. Ng and Jiawei Han, CLARANS: A Method for Clustering Objects for Spatial Data Mining, IEEE TRANSACTIONS ON KNOWLEDGE and DATA ENGINEERING, Vol. 14, No. 5,
  17. Shai Ben-David, D´avid P´al, and Hans Ulrich Simon. Stability of k-means clustering. Lecture Notes in Computer Science, 4539:20–34, 2007
  18. Shekhar, S. , and Chawla, S. 2003. Spatial Databases A Tour. Prentic e Hall (ISBN 0-7484-0064-6).
  19. Tapas Kanungo, David M. Mount, Nathan S. Ne-tanyahu, Christine D. Piatko, Ruth Silverman, and An-gela Y. Wu. An efficient k-means clustering algorithm Analysis and implementation. IEEE Trans. Pattern Anal. Mach. Intell. , 24(7):881–892, 2002.
Index Terms

Computer Science
Information Sciences

Keywords

Spatial data mining Spatial database K-mean Spatial relationship Datamining

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