International Journal of Knowledge Based Computer System

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Experimental Study of Data Mining Classification Algorithms in Establishing Indian Agricultural Commodity Patterns


Affiliations
1 Department of Computer Science, Government First Grade College, Yelahanka, Bangalore, Karnataka., India
     

   Subscribe/Renew Journal


This paper presents novel idea of how to establish various products pattern of Indian agricultural commodity using Data Mining Classification Algorithms. Generally when we talk about Data Mining we come across several basics and advance technique's to incorporate for the broader applications of usage. Now we want to use Data Mining algorithms to extract some very interesting patterns by detailed study of agricultural data sets. As we all know computing and information has vast scope to deal for commercial usage but picture changes when it comes to medium profitable segments. In this paper I have tried experimental basis of Data Sets using agricultural products category by extracting from local APMC (Agricultural Product Market Cooperation). This organization helps locally to guide farmers to know the best price for selling and buying. If we incorporate new trend setting solution that makes more transparent and predictable solution patterns for farmers of local community and compare the price segments with national monitoring markets like Agmark(Agricultural market of India). The research establishes various classifications based on given class of market by using Naïve Bayes and Bayes Net algorithms and comparing with Rules one R [1R] and Trees.J48.

Keywords

Data Mining, Classification, Algorithms, Knowledge, Data Engineering Tools, Techniques, Agricultural Products, Agricultural Markets
Subscription Login to verify subscription
User
Notifications
Font Size


  • Baik, S. & Bala, J. (2004). A Decision Tree Algorithm for Distributed Data Mining: Towards Network Intrusion Detection, Lecture Notes in Computer Science, 3046, 206-212.

  • Bouckaert, R. (2004). Naive Bayes Classifiers That Perform Well with Continuous Variables, Lecture Notes in Computer Science, 3339, 1089-1094.

  • Bouckaert, R. R. (2008). Bayesian Network Classifiers in Weka. Retrieved from remco@ cs.waikato.ac.nz.

  • Breslow, L. A. & Aha, D. W. (1997). Simplifying decision trees: A survey. Knowledge Engineering Review 12(1), 1–40.

  • Brighton, H. & Mellish, C. (2002). Advances in Instance Selection for Instance-Based Learning Algorithms. Data Mining and Knowledge Discovery, 6(2), 153-172.

  • Cheng, J. & Greiner, R. (2001). Learning Bayesian Belief Network Classifiers: Algorithms and System. In Stroulia, E. & Matwin, S. (ed.), AI 2001, 141-151, LNAI 2056.

  • Cheng, J., Greiner, R., Kelly, J., Bell, D. & Liu, W. (2002). Learning Bayesian networks from data: An information-theory based approach. Artificial Intelligence, 137(1-2), 43-90.

  • Clark, P. & Niblett, T. (1989). The CN2 Induction Algorithm. Machine Learning, 3(4), 261-283.

  • Cover, T. & Hart, P. (1967). Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory, 13(1), 21-7.

  • Cowell, R. G. (2001). Conditions under Which Conditional Independence and Scoring Methods Lead to Identical Selectionof Bayesian Network Models. Proceedings of the 17th International Conference on Uncertainty in Artificial Intelligence.

  • Domingos, P. & Pazzani, M. (1997). On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning, 29(2-3), 103-130.

  • Elomaa, T. (1999). The biases of decision tree pruning strategies. Lecture Notes in Computer Science (1642, pp. 63-74). Springer.

  • Friedman, N. & Koller, D. (2003). Being Bayesian about Network structure: A Bayesian approach to structure discovery in Bayesian networks. Machine Learning, 50(1), 95-125.

  • Friedman, N., Geiger, D. & Goldszmidt, M. (1997). Bayesian network classifiers. Machine Learning, 29(2-3), 131-163.

  • Han, J. & Kamber, M. (2011). Data Mining Concepts and Techniques (2nd Ed.). Elsevier.

  • Jensen, F. (1996). An Introduction to Bayesian Networks. Springer.

  • Kubat, M. & Cooperson M. (2001). A reduction technique for nearest-neighbor classification: Small groups of examples. Intelligent Data Analysis, 5(6), 463-476.

  • Madden, M. (2003). The Performance of Bayesian Network Classifiers Constructed using Different Techniques. Proceedings of European Conference on Machine Learning.

  • McSherry, D. (1999). Strategic induction of decision trees. Knowledge-Based Systems, 12(5- 6), 269-275.

  • Vivarelli, F. & Williams, C. (2001). Comparing Bayesian neural network algorithms for classifying segmented outdoor images. Neural Networks 14(4-5), 427-437.

  • Weka: Data Mining Software in JAVA (2013). Retrieved from http://www.cs.waikato.ac.nz/ml/weka.

  • Wilson, D. R. & Martinez, T. (2000). Reduction techniques for instance-based learning algorithms. Machine Learning, 38, 257-286.

  • Workshop on Probabilistic Graphical Models for Classification, pp. 59-70.

  • Written, I. H. & Frank, E. (2007). Data Mining Practical Machine Learning Tools and Techniques (2ndEd.). Elsevier.

  • Yang, Y. & Webb, G. (2003). On why discretisization Works for Naive-Bayes Classifiers. Lecture Notes in Computer Science, 2903, 440-452.

  • Zhang, G. (2000). Neural Networks for Classification: A Survey. IEEE transactions on systems, man, and cybernetics, 30(4), 451-462.

  • Zheng, Z. (2000). Constructing X-of-N attributes for decision tree learning. Machine Learning, 40(1), 35-75.


Abstract Views: 546

PDF Views: 4




  • Experimental Study of Data Mining Classification Algorithms in Establishing Indian Agricultural Commodity Patterns

Abstract Views: 546  |  PDF Views: 4

Authors

Gulledmath Sangayya
Department of Computer Science, Government First Grade College, Yelahanka, Bangalore, Karnataka., India

Abstract


This paper presents novel idea of how to establish various products pattern of Indian agricultural commodity using Data Mining Classification Algorithms. Generally when we talk about Data Mining we come across several basics and advance technique's to incorporate for the broader applications of usage. Now we want to use Data Mining algorithms to extract some very interesting patterns by detailed study of agricultural data sets. As we all know computing and information has vast scope to deal for commercial usage but picture changes when it comes to medium profitable segments. In this paper I have tried experimental basis of Data Sets using agricultural products category by extracting from local APMC (Agricultural Product Market Cooperation). This organization helps locally to guide farmers to know the best price for selling and buying. If we incorporate new trend setting solution that makes more transparent and predictable solution patterns for farmers of local community and compare the price segments with national monitoring markets like Agmark(Agricultural market of India). The research establishes various classifications based on given class of market by using Naïve Bayes and Bayes Net algorithms and comparing with Rules one R [1R] and Trees.J48.

Keywords


Data Mining, Classification, Algorithms, Knowledge, Data Engineering Tools, Techniques, Agricultural Products, Agricultural Markets

References