CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Comparative Study of Spatial Data Mining Techniques

by Kamalpreet Kaur Jassar, Kanwalvir Singh Dhindsa
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 112 - Number 14
Year of Publication: 2015
Authors: Kamalpreet Kaur Jassar, Kanwalvir Singh Dhindsa
10.5120/19734-1528

Kamalpreet Kaur Jassar, Kanwalvir Singh Dhindsa . Comparative Study of Spatial Data Mining Techniques. International Journal of Computer Applications. 112, 14 ( February 2015), 19-22. DOI=10.5120/19734-1528

@article{ 10.5120/19734-1528,
author = { Kamalpreet Kaur Jassar, Kanwalvir Singh Dhindsa },
title = { Comparative Study of Spatial Data Mining Techniques },
journal = { International Journal of Computer Applications },
issue_date = { February 2015 },
volume = { 112 },
number = { 14 },
month = { February },
year = { 2015 },
issn = { 0975-8887 },
pages = { 19-22 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume112/number14/19734-1528/ },
doi = { 10.5120/19734-1528 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:49:28.487908+05:30
%A Kamalpreet Kaur Jassar
%A Kanwalvir Singh Dhindsa
%T Comparative Study of Spatial Data Mining Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 112
%N 14
%P 19-22
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Spatial data mining is a mining knowledge from large amounts of spatial data. Spatial data mining algorithms can be separated into four general categories: clustering and outlier detection, association and co-location method, trend detection and classification. All these methods have been compared according to various attributes. This paper introduces the fundamental concepts of widely known spatial data mining algorithms in a comparative way. It focuses on techniques and their unique features.

References
  1. 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, Sept-Oct 2002.
  2. N. Sumathi, R. Geetha and Dr. S. Sathiya Bama, "spatial data mining-techniques trends and its applications", Journal of Computer Applications, Vol. 1, No. 4, Oct – Dec 2008.
  3. Maria Halkidi, Yannis Batistakis and Michalis Vazirgiannis, "On Clustering Validation Techniques", Journal of Intelligent Information Systems, Vol. 17, pp. 107–145, 2001.
  4. Deepti Sisodia, Lokesh Singh, Sheetal Sisodia and Khushboo Saxena. "Clustering Techniques: A Brief Survey of Different Clustering Algorithms", International Journal of Latest Trends in Engineering and Technology (IJLTET), Vol. 1 Issue 3 Sept 2012.
  5. Raymond T. Ng and Jiawei Han, "Efficient and Effective Clustering Methods for Spatial Data Mining", IEEE Computer Society.
  6. P. Murugavel and Dr. M. Punithavalli, "Improved Hybrid Clustering and Distance-based Technique for Outlier Removal", International Journal on Computer Science and Engineering, Vol. 3, No. 1, Jan 2011.
  7. T. Dasu and T. Johnson "Exploratory Data Mining and Data Cleaning", 2003.
  8. M. D. Boomija, "Comparison of Partition Based Clustering Algorithms", Journal of Computer Applications, Vol. 1, No. 4, Oct – Dec 2008.
  9. Dr. M. Hemalatha and N. Naga Saranya " A Recent Survey on Knowledge Discovery in Spatial Data Mining" , International Journal of Computer Science Issues, Vol. 8, Issue 3, No. 2, May 2011.
  10. Shu-Hsien Liao, Pei-Hui Chu and Pei-Yuan Hsiao, "Data mining techniques and applications", Science Direct Expert Systems with Applications, Vol. 39, Issue 12, 15 Sept 2012.
  11. Berry, M. J. A. and Linoff, G,"Data Mining Techniques For Marketing, Sales and Customer Support", 1996.
  12. D. Guo and J. Mennis, "Spatial Data Mining and geographic knowledge discovery-An introduction", Computers, Environment and Urban Systems 33, 2009.
  13. O. A. Abbas, "Comparison between Data Clustering algorithms", International Arab Journal of Information Technology, Vol. 5. No. 3, July 2008.
  14. Sundararajan S, Dr. Karthikeyan S," A Study On Spatial Data Clustering Algorithms In Data Mining", International Journal Of Engineering And Computer Science, Volume1 Issue 1 Oct 2012,pp. 37-41.
  15. Matheus C. J, Chan P. K, and Piatetsky-Shapiro G. 1993. "Systems for Knowledge Discovery in Databeses", IEEE Transactions on Knowledge and Data Engineering 5(6):903-913.
  16. 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, Aug 1996.
  17. Davies, D. L. and Bouldin, D. W. (1979). A Cluster Separation Measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 224–227.
  18. Han, J. and Kamber, M. (2001). Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, USA.
  19. Gueting R. H. 1994. An Introduction to Spatial Database Systems, The VLDB Journal, pp. 357-399.
  20. A. B. M. S. Ali and K. A. Smith , On learning algorithm for classification, Applied Soft Computing, Dec 2004. pp. 119-138.
  21. Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceeding of the 20th VLDB conference, pp 487–499.
  22. Jiawei Han and Michheline Kamber,Data mining concepts and techniques-a reffrence book , pp. 383-422.
Index Terms

Computer Science
Information Sciences

Keywords

Spatial clustering Clustering and Outlier Detection Association and Co-Location Classification Trend-Detection Clustering algorithms Knowledge Discovery in Database

Powered by PhDFocusTM