Abstract
The Sri Lankan Journal of Applied Statistics(SLJAS) is an open-access, international, double-blind peer-reviewed journal published by the Institute of Applied Statistics, Sri Lanka (IASSL). The main purpose of the journal is to publish the results of original work on applications of Statistics and on theoretical and methodical aspects of Statistics. The journal also welcomes critical reviews including conceptual discussions, opinions and book reviews. Applications of Statistics in the area of Agriculture & Forestry, Medical, Dental and Veterinary Sciences, Natural, Physical Sciences, Social Sciences, Economics and Actuarial Science fall within the scope of the journal. This journal does not charge any fee for article processing and submission.
Highlights
Clustering of k-means forms part of the main topics in machine learning
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
We present an application of clustering analysis to Agronomy, with eight varieties
Summary
Clustering of k-means forms part of the main topics in machine learning. Machine learning is widely used in physical or natural sciences, as it helps to get an intuition about the structure and pattern of the data. Clustering identifies similar or different subgroups in a given dataset (Hartigan and Wong, 1976). The homogeneity identifies similar clusters according to their data points. The k-means method uses a prototype (centroids) to represents clusters by optimizing the squared error function (Bradley, et al, 1998). It is considered an iterative algorithm because the data, is partitioned into clusters (subgroups), thereby making the data points as similar (homogeneous) as possible (Bradley & Fayyad, 1998). Fuzzy k-means, on the other hand, is regarded as a soft (flexible) method than k-means because each point can belong to two centroids with different quality (Bradley & Fayyad, 1998). A summary of the dataset is presented in the methodology, followed by results and discussion.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
