Geographical Knowledge report on Agriculture by Spatial Data Mining
DOI:
https://doi.org/10.37591/joadms.v5i1.1403Keywords:
Data mining, Geographical Information System, Spatial Data mining,Abstract
Spatial Data Mining is nothing but the integration of Geographical Information System and Data Mining. The main objective of the present study i.e., Spatial Data Mining for Agriculture is to find or identify the potentially useful and ultimately understandable patterns in data which will be visualized on the map. Data Mining Algorithms such as clustering, classification, and association rules etc are applied for the Past and Future trends visualization for Agriculture on the Map and also to identify the useful information or decision-making knowledge in the database and extracting these in such a way that they can be put to use in areas such as decision support, prediction, forecasting, and estimation.
References
Krzysztof Koperski.; Junas Adhikary.; and Jiawei Han. Spatial Data Mining: Progress and Challenges Survey Paper, School of Computer Science Simon Fraser University Burnaby, B.C.Canada V5A IS6.
M.Hemalatha.M; Naga Saranya.N. A Recent Survey on Knowledge Discovery in Spa-tial Data Mining, IJCI International Journal of Computer Science, Vol 8, Issue 3, No.2, may,2011.
Jianwei Li;, Ying Liu;, Wei-keng Liao;, Alok Choudhary. Parallel Data Mining Algo-rithms for Association Rules and Clustering.
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 and R Srikant. Fast Algorithms for Mining Association Rules. In Proc. Of Very Large Databases, may 1994.
J. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, 1993.
S.Shekhar and S.Chawla. Spatial Databases: A Tour. Pretice Hall (ISBN 0-7484-0064-6), 2003.
R. Ng and J. Han. (1994) Effective and Efficient Clustering Methods for Spatial Data Mining, Technical Report 94-13, University of British Columbia.
H. Samet. (1990) The Design and Analysis of Spatial Data Structures, Addison-Wesley.
Agrawal R., Imielinski T., and Swami A. 1993 “Database Mining: A Performance Perspective”, IEEE Transactions on Knowledge and Data Engineering, Vol. 5, No. 6, pp. 914- 925.
Ester M., Frommelt A., Kriegel H.-P., and Sander J. 1998 “Algorithms for Characteri-zation and Trend Detection in Spatial Databases”, Proc. 4th Int. Conf. on Knowledge Discovery and Data Mining, New York City, NY, pp. 44-50.
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