Principal Component based Fuzzy c-means Algorithm for Clustering Lentil Germplasm

摘要

Cluster analysis is used extensively to organize data into groups based on similarities among the individual data items, leading to a crisp or fuzzy partition of sample space. Fuzzy c-means (FCM) is a clustering algorithm which all owsone data point to be long to two or more clusters. In this paper, principal component based fuzzy c-means clustering is applied for classifying 518 lentil genotypes based on their numeric agronomic and morphological traits. The appropriate number of clusters is obtained with the help of validity measures. Results of the study revealed that the genetic divergence is not highly related to geographical origins as exotic and indigenous lentil genotypes are distributed in all the four clusters.