Abstract
Agriculture is one of the most important occupations carried out by centuries - old people. Agriculture is our nation’s backbone occupation. This is actually the occupation that fulfills the people’s food needs. In this paper, we examine certain data mining techniques (Association rule) in the field of agriculture. Some of these techniques are explained, such as Apriori algorithm, K-nearest neighbor and K-means and the implementation of these techniques is represented in this field. In this field the effectiveness of the Associative rule is successful. In view of the details of agricultural soils and other data sets, this paper represents the role of data mining (association rule). This paper therefore represents the various algorithms and data mining techniques in agriculture.
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The authors express gratitude towards the assistance provided by Accendere Knowledge Management Services Pvt. Ltd. In preparing the manuscripts. We also thank our mentors and faculty members who guided us throughout the research and helped us in achieving desired results.
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Vignesh, N., Vinutha, D.C. (2020). Association Rule Data Mining in Agriculture – A Review. In: Smys, S., Tavares, J., Balas, V., Iliyasu, A. (eds) Computational Vision and Bio-Inspired Computing. ICCVBIC 2019. Advances in Intelligent Systems and Computing, vol 1108. Springer, Cham. https://doi.org/10.1007/978-3-030-37218-7_27
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DOI: https://doi.org/10.1007/978-3-030-37218-7_27
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