International Journal of Statistics and Applied Mathematics
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International Journal of Statistics and Applied Mathematics

2020, Vol. 5, Issue 4, Part A

Comparison of clustering techniques used in divergence analysis


Author(s): Adarsh VS, Dr. Brigit Joseph and Pratheesh P Gopinath

Abstract: Cluster analysis has been generally used as an efficient tool in the quantitative estimation of genetic diversity for a breeding programme (divergence analysis), grouping of different genotypes of a particular crop and it is more useful in choosing suitable parents for heterosis breeding. Clustering techniques aims to group data according to common properties. This grouping is often based on the distance between the data. Clustering techniques are divided into hierarchical and non-hierarchical methods according to the fragmentation technique of clusters. There are various clustering methods which come under these techniques viz., single linkage, complete linkage, average linkage, Ward’s method and Tocher method. The efficiency of the techniques and methods are given less importance. In this context an attempt was made in this paper for the comparative study of the different clustering techniques and methods in small samples. The cluster validation techniques are used for the efficiency measurements of these methods. The results of the analysis are presented comparatively at the end of the study and which methods are more convenient for data set is explained.

Pages: 19-21 | Views: 82 | Downloads: 7

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How to cite this article:
Adarsh VS, Dr. Brigit Joseph, Pratheesh P Gopinath. Comparison of clustering techniques used in divergence analysis. Int J Stat Appl Math 2020;5(4):19-21.
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