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

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Title Principal Component based Fuzzy c-means Algorithm for Clustering Lentil Germplasm
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Creator Chiranjit Mazumder
Girish K. Jha
Rajender Parsad
Anshu Bharadwaj
Jyoti Kumar
 
Subject FCM algorithm
Fuzzy clustering
Lentil
Validity measures
 
Description Not Available
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.
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Date 2017-04-13T05:45:21Z
2017-04-13T05:45:21Z
2015-12-31
 
Type Research Paper
 
Identifier Mazumder, C., Jha,G.K., Parsad, Rajender, Bhardwaj, A. and Kumari, J. (2015). Principal component based fuzzy c-means algorithm for clustering lentil germplasm. Journal of the Indian Society of Agricultural Statistics, 69(3), 307-314.
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http://krishi.icar.gov.in/jspui/handle/123456789/3607
 
Language English
 
Relation Not Available;
 
Publisher Indian Society of Agricultural Statistics