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A Brief Survey of Data Mining Techniques Applied to Agricultural Data
AR. PonPeriasamy, E. Thenmozhi

Published in: International Journal of Computer Sciences and Engineering
Volume- 5, Issue-4, pp.129-132, Apr 2017
DPI :-> 16.10022.IJCSE.2017.V5I4.129132.1537



Abstract
As with many other sectors the amount of agriculture data based are increasing on a daily basis. However, the application of data mining methods and techniques to discover new insights or knowledge is a relatively a novel research area. In this paper we provide a brief review of a variety of Data Mining techniques that have been applied to model data from or about the agricultural domain. The Data Mining techniques applied on Agricultural data include k-means, bi clustering, k nearest neighbor, Neural Networks (NN) Support Vector Machine (SVM), Naive Bayes Classifier and Fuzzy c-means. As can be seen the appropriateness of data mining techniques is to a certain extent determined by the different types of agricultural data or the problems being addressed. This survey summarize the application of data mining techniques and predictive modeling application in the agriculture field.

Key-Words / Index Term
Agriculture, Data Mining, k-means, bi clustering, Support Vector Machine and Naive

How to cite this article
AR. PonPeriasamy, E. Thenmozhi , “A Brief Survey of Data Mining Techniques Applied to Agricultural Data”, International Journal of Computer Sciences and Engineering, 5, Issue-4, pp.129-132, Apr 2017. DPI:16.10022.IJCSE.V5.I4.1537