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

• 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.
• 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.
• Maria Halkidi, Yannis Batistakis and Michalis Vazirgiannis, "On Clustering Validation Techniques", Journal of Intelligent Information Systems, Vol. 17, pp. 107–145, 2001.
• 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.
• Raymond T. Ng and Jiawei Han, "Efficient and Effective Clustering Methods for Spatial Data Mining", IEEE Computer Society.
• 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.
• T. Dasu and T. Johnson "Exploratory Data Mining and Data Cleaning", 2003.
• M. D. Boomija, "Comparison of Partition Based Clustering Algorithms", Journal of Computer Applications, Vol. 1, No. 4, Oct – Dec 2008.
• 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.
• 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.
• Berry, M. J. A. and Linoff, G,"Data Mining Techniques For Marketing, Sales and Customer Support", 1996.
• D. Guo and J. Mennis, "Spatial Data Mining and geographic knowledge discovery-An introduction", Computers, Environment and Urban Systems 33, 2009.
• O. A. Abbas, "Comparison between Data Clustering algorithms", International Arab Journal of Information Technology, Vol. 5. No. 3, July 2008.
• 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.
• 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.
• 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.
• Davies, D. L. and Bouldin, D. W. (1979). A Cluster Separation Measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 224–227.
• Han, J. and Kamber, M. (2001). Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, USA.
• Gueting R. H. 1994. An Introduction to Spatial Database Systems, The VLDB Journal, pp. 357-399.
• A. B. M. S. Ali and K. A. Smith , On learning algorithm for classification, Applied Soft Computing, Dec 2004. pp. 119-138.
• Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceeding of the 20th VLDB conference, pp 487–499.
• Jiawei Han and Michheline Kamber,Data mining concepts and techniques-a reffrence book , pp. 383-422.