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

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).