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Application of K-Means and Fuzzy K-Means to Rice Dataset in Sierra Leone

Authors:

R. M. Bangura ,

Sierra Leone Agricultural Research Institute, Freetown, SL
About R. M.
Biometric Unit

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S. D. Johnson,

Rokupr Agricultural Research Centre, Kambia district, SL
About S. D.
Agronomy Department

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O. Mbulayi

University of Kinshasa, CD
About O.
Mathematics and Computer Science
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Abstract

As k-means and fuzzy k-means are regarded as unsupervised dimensional reduction learning techniques, we present an application of this technique from the Agronomic data collected in 2015 to demonstrate the efficiency of fuzzy k means over k means of eight different types of rice varieties in Sierra Leone. Also, we identified different rice varieties as outliers from the silhouette clusters (segment).
How to Cite: Bangura, R.M., Johnson, S.D. and Mbulayi, O., 2020. Application of K-Means and Fuzzy K-Means to Rice Dataset in Sierra Leone. Sri Lankan Journal of Applied Statistics, 21(3), pp.69–73. DOI: http://doi.org/10.4038/sljastats.v21i3.8062
Published on 31 Dec 2020.
Peer Reviewed

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