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Recommendation In Agronomical Data Using Data Mining Techniques

Authors: Urvashi*1 & Dr. Kanwal Garg2;

Recommendation In Agronomical Data Using Data Mining Techniques

Abstract

Agriculture is the backbone of Indian economy. In India various crops are grown such as cotton, wheat, rice, mustard, tea, coffee. The land of agronomical people is distributed into four categories such as small land holding farmer, moderate land holding farmer, high land holding farmer and landless farmers. Around 70% farmers are belongs to the small land holding farmer category. This reflects that majority of farmers having less area of growing. Farmer directly depends on the productivity of the crop. So, this paper is focused to perform Feature Selection and yield prediction of the crop by implementing Clustering algorithm.

Keywords

Agriculture, Yield prediction, Data Mining, K-Means, Improved K-Means, K-medoid, Clustering, Matlab, Feature Selection.

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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