Indian Journal of Science and Technology
DOI: 10.17485/ijst/2016/v9i19/93873
Year: 2016, Volume: 9, Issue: 19, Pages: 1-4
Original Article
V. Rajeswari* and K. Arunesh
Department of Computer Science, Sri SRNM College, Sattur - 626203, Virudhunagar Dist., Tamil Nadu, India; [email protected], [email protected]
*Author for correspondence
V. Rajeswari
Department of Computer Science, Sri SRNM College, Sattur - 626203, Virudhunagar Dist., Tamil Nadu, India; [email protected]
Background/Objectives: Soil is an essential key factor of agriculture. The objective of the work is to predict soil type using data mining classification techniques. Methods/Analysis: Soil type is predicted using data mining classification techniques such as JRip, J48 and Naive Bayes. These classifier algorithms are applied to extract the knowledge from soil data and two types of soil are considered such as Red and Black. Findings: In this paper, Data Mining and agricultural Data Mining are summarized. The JRip model can produce more reliable results of this data and the Kappa Statistics in the forecast were increased. Application/Improvement: For solving the issues in Big Data, efficient methods can be created that utilize Data Mining to enhance the exactness of classification of huge soil data sets.
Keywords: Data Mining, Naive Bayes, J48, JRip, Soil Dataset
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