Expert System for Land Suitability Evaluation using Data mining‘s Classification Techniques: a Comparative Study

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2016 by IJCTT Journal
Volume-33 Number-2
Year of Publication : 2016
Authors : C.Parthiban, M.Balakrishnan
  10.14445/22312803/IJCTT-V33P119

MLA

C.Parthiban, M.Balakrishnan "Expert System for Land Suitability Evaluation using Data mining‘s Classification Techniques: a Comparative Study". International Journal of Computer Trends and Technology (IJCTT) V33(2):87-92, March 2016. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
Data mining involves the extraction of implicit, “interesting” information from a database. Classification is an important Data mining’s “machine learning” technique which is used to predict data instances from dataset. It involves the order wise analysis of large amount of information sets. Data mining applications are used in various areas such as health care, insurance, medicines, Agriculture, banking and soil management. In soil region the Data mining mainly used to classify the soil and predicting the land suitability for the crop and fertilizer recommendation. The purpose of this study is to predict the land suitability for the crop using classification algorithms namely Naive Bayes and J48. This work focused on find out the best classification algorithm based on accuracy measure, performance measure, error rate and execution time using the soil dataset. From the experimental result using WEKA tool it is observed that the performance of the J48 is better than the Naive Bayes algorithm.

References
1. F. N. Afrati, A. Gionis, and H. Mannila. ―Approximatinga Collection of Frequent Sets. In Proc. 2004 ACMSIGKDD Int. Conf. Knowledge Discovery in Databases(KDD’04), pp. 12-19, Seattle, WA, Aug. 2004.
2. BalakrishnanM., Application of Data Mining Techniques in Agriculture,Training Manual, National Academy of Agricultural Research Management, Hyderabad.pp1-?
3. S. Muggleton, Inductive Acquisition of Expert Knowledge, Addison-Wesley, Reading, Mass, USA, 1990.
4. Halil Akıncı , Ays_e Yavuz Ozalp , Bulent Turgut, Agricultural land use suitability analysis using GIS and AHP technique, Computers and Electronics in Agriculture,vol.97, pp.71-82.
5. AN.Ganeshamurthy, R.Dinesh, N.Ravisankar, AK.Nair, SPS.Ahalwat, Land Resources of Andaman and Nicobar Islands, Central Agricultural Research Institute, (ICAR).
6. Say, N.P., Yucel, M., and Yilmazer, M., A Computer-based System for Environmental Impact Assessment (EIA) Applications to Energy Power Stations in Turkey: CEDINFO, Journal of Energy Policy, Vol. 35, pp.6395-6401, 2007.
7. Jiawei Han and Micheline Kamber, Data Mining Concepts and Techniques, 2nd ed., Morgan Kaufmann publishers, SanFrancisco, 2006.
8. Sunita B Aher, Lobo LMRJ, Data Mining in Educational System using Weka, International Conference on Emerging Technology Trends (ICETT), Proceedings published by International Journal of Computer Applications (IJCA) Number 3, 2011, pp-20-25.
9. D. Pedro and M. Pazzani "On the optimality of the simple Bayesian classifier under zero-one loss". Machine Learning, 29:103–137, 1997.
10. S. Vijayarani, Mr.S.Dhayanand, Data mining classification algorithms for kidney disease prediction, International Journal on Cybernetics & Informatics (IJCI) Vol. 4, No. 4, August 2015, pp.13- 25.
11. Uffe B. Kjærulff, Anders L. Madsen, Probabilistic Networks — an Introduction to Bayesian Networks and Influence Diagrams, May 2005.
12. Zhang H.; Su J.; (2004) ―Naive Bayesian classifiers for ranking. Paper appeared in ECML2004 15th European Conference on Machine Learning, Pisa, Italy
13. http://www.c4.5-Wikipedia, the free encyclopedia.htm accessed on 16/12/2010.
14. J. R. Quinlan ―C4.5: programs for machine learning Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1993.
15. Say, N.P., Yucel, M., and Yilmazer, M., 2007,A Computer-based System for Environmental Impact Assessment (EIA) Applications to Energy Power Stations in Turkey: CEDINFO, Journal of Energy Policy, Vol. 35, pp.6395-6401.
16. Ramesh Vamanan., K.Ramar. (2011), Classification of Agricultural Land Soils of Data Mining Approach, International Science on Computer Science and Engineering (IJCSE), ISSN: 0975-3397 Vol.3 No. 1 Jan 2011, pp. 379-383.
17. S. Muggleton, Inductive Acquisition of Expert Knowledge, Addison-Wesley, Reading, Mass, USA, 1990.
18. Swartout W., and Moore J. (1993), Explanation in Second Generation Expert Systems. In David J., Krivine, J-P., and Simmons R., Editors, Second Generation Expert Systems, pp. 543-585. Springer Verlag.
19. Lemmon, H. (1986), COMAX : An Expert System for Cotton Crop Management, Science 233 (4759), pp. 29-33.
20. Charu C. Agarwal, Data Classification Algorithm and Applications (An Introduction to Data Classification),IBM T. J. Watson Research CenterYorktown Heights, New York, USA.

Keywords
WEKA, Data Mining, Naïve Bayes, J48, Soil Dataset, Classification Algorithm.