scholarly journals Morphological classification of genetic diversity in cultivated okra, Abelmoschus esculentus (L) Moench using principal component analysis (PCA) and single linkage cluster analysis (SLCA)

2011 ◽  
Vol 10 (54) ◽  
pp. 11165-11172 ◽  
Author(s):  
C Nwangburuka C ◽  
B Kehinde O ◽  
K Ojo D ◽  
A Denton O ◽  
R Popoola A
Keyword(s):  
Genetic Diversity ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Principal Component ◽  
Abelmoschus Esculentus ◽  
Morphological Classification ◽  
Single Linkage ◽  
Single Linkage Cluster ◽  
Linkage Cluster

Genetic Diversity Assessment in Dolichos Bean (Lablab purpureus L.) Based on Principal Component Analysis and Single Linkage Cluster Analysis

2021 ◽  
Author(s):  
S.R. Singh ◽  
S. Rajan ◽  
Dinesh Kumar ◽  
V.K. Soni
Keyword(s):  
Genetic Diversity ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Single Linkage ◽  
Pod Yield ◽  
Diversity Assessment ◽  
Single Linkage Cluster ◽  
Linkage Cluster

Background: Dolichos bean occupies a unique position among the legume vegetables of Indian origin for its high nutritive value and wider climatic adaptability. Despite its wide genetic diversity, no much effort has been undertaken towards genetic improvement of this vegetable crop. Knowledge on genetic variability is an essential pre-requisite as hybrid between two diverse parental lines generates broad spectrum of variability in segregating population. The current study aims to assess the genetic diversity in dolichos genotypes to make an effective selection for yield improvement.Methods: Twenty genotypes collected from different regions were evaluated during year 2016-17 and 2017-18. Data on twelve quantitative traits was analysed using principal component analysis and single linkage cluster analysis for estimation of genetic diversity.Result: Principal component analysis revealed that first five principal components possessed Eigen value greater than 1, cumulatively contributed greater than 82.53% of total variability. The characters positively contributing towards PC-I to PC-V may be considered for dolichos improvement programme as they are major traits involved in genetic variation of pod yield. All genotypes were grouped into three clusters showing non parallelism between geographic and genetic diversity. Cluster-I was best for earliness and number of cluster/plant. Cluster-II for vine length, per cent fruit set, pod length, pod width, pod weight and number of seed /pod, cluster III for number of pods/cluster and pod yield /plant. Selection of parent genotypes from divergent cluster and component having more than one positive trait of interest for hybridization is likely to give better progenies for development of high yielding varieties in Dolichos bean.

scholarly journals Assessment of Genetic Divergence in Long Day Onion (Allium cepa L.) through Principal Component and Single Linkage Cluster Analysis

2020 ◽  
Vol 15 (1) ◽  
pp. 17-26
Author(s):  
Singh S R ◽  
Ahamed N ◽  
Srivastava K K ◽  
Kumar D ◽  
Yousuf S
Keyword(s):  
Cluster Analysis ◽  
Principal Component ◽  
Single Linkage ◽  
Long Day ◽  
Wide Range ◽  
Positive Loading ◽  
High Positive Loading ◽  
Single Linkage Cluster ◽  
Linkage Cluster ◽  
Bulb Yield

To assess the nature and magnitude of genetic diversity in long day onion germplasm by using the principal component analysis and single linkage cluster analysis an experiment was carried out with 34 onion genotypes. High coefficient of variation with wide range in traits indicated an appreciable variability in germplasm. Genotypes were classified into seven principal components having Eigen value > 1, cumulatively accounted for 83.87% of total variability. Principal Component - I contributed for 24.73% of total variation for followed by principal component-II (15.27%). PC-I had high positive loading for bulb weight (0.401), marketable yield (0.338), total bulb yield (0.401) and PC-II had high positive loading for plant height (0.412), PC-III for high T.S.S. (0.276) PC -IV for A grade bulbs (0.436), PC-V for polar diameter of bulbs (0.514), PC-IV negatively loaded with purple blotch (-0.461) and PC-VII for narrow neck thickness (-0 .515). Plotting PC-I aganist PC-II differenciated CITH-O-13, CITH-O-4, CITH-O-22, CITH-O-19, CITH-O-9, CITH-O-6 and CITH-O2 as most divergent genotype.On the basis of single linkage cluster means cluster-I was most importent for average bulb weight, minimum bolters, high marketble bulb percentage high marketable and total bulb yield whereas cluster -II was important for maximum nuber of leaves/plant and minimum neck thicknes. Highest inter-cluster distance was observed between cluter II and Cluster-I(873.5% ).Most divergent genotypes with high inter cluter distance could be the most appropriate parents for crop impovement in onion.

Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 30
Author(s):  
Hyeuk Kim
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Unsupervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Baseball Players ◽  
Partitioning Around Medoids ◽  
Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

↵Genetic Diversity and Principal Component Analysis in Mungbean [Vigna radiata (L.) Wilczek] under Rainfed Condition

2021 ◽  
Author(s):  
Deepak Gupta ◽  
Suresh Muralia ◽  
N.K. Gupta ◽  
Sunita Gupta ◽  
M.L. Jakhar ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Seed Yield ◽  
Agricultural Research ◽  
Principal Component ◽  
Component Analysis ◽  
Grain Legume ◽  
Research Station ◽  
Rainfed Condition

Background: Mungbean is a short duration grain legume widely grown in south and Southeast Asia. The extent of variability through Principal Component Analysis (PCA) and cluster analysis in promising mungbean genotypes should be known for possible yield improvement. A study was undertaken to work out the extent of variability among twenty four mungbean genotypes through cluster analysis and Principal Component Analysis (PCA). Methods: The experiment was laid out in a randomized block design with three replications during kharif 2018 and 2019 at the experimental field of Agricultural Research Station, Navgaon (Alwar) under rainfed condition. Result: Principal component analysis revealed that the first three main PCAs amounted 78.80% of the total variation among genotypes for different traits. Out of total principal components, PC1 accounts for maximum variability in the data with respect to succeeding components. Number of branches per plant (28.62%), number of clusters per plant (23.55%) and seed yield (15.58%) showed maximum per cent contribution towards total genetic divergence on pooled basis. Cluster analysis showed that genotypes fall into seven different clusters and their inter and intra cluster distance showed genetic diversity between different genotypes. The maximum number of genotypes i.e., 8 was found in cluster II followed by cluster III comprising of 6 genotypes. Genotypes RMG-1138 and IPM-02-03 representing the mono genotypic cluster signifies that it can be the most diverse variety and it would be the appropriate genotype for hybridization with ones present in other clusters to tailor the agriculturally important traits and ultimately to boost the seed yield in mungbean under rainfed conditions.

Genetic variability determination in garden pea (Pisum sativum L sub sp. hortense Asch. and Graebn.) by using the multivariate analysis

2017 ◽  
Author(s):  
S.R. Singh ◽  
N. Ahmed ◽  
D. B. Singh ◽  
K. K. Srivastva ◽  
R. K. Singhand Abid Mir
Keyword(s):  
Genetic Variability ◽  
Plant Height ◽  
Principal Component ◽  
Single Linkage ◽  
Pod Yield ◽  
Yield And Quality ◽  
Single Linkage Cluster ◽  
Positive Traits ◽  
Linkage Cluster ◽  
Pod Length

A total of 32 genotypes collected from different geographical areas evaluated at one site to determine the genetic variability. Considerable diversity was found in different traits of horticultural importance. Principal component analysis showed more than 84 % of total multivariate variation for important traits in different genotypes. Pod yield, pod length,10 pods weight and pods per plant were found to be major traits contributing towards principal component-I. Similarly, seeds per pod, secondary branches/plant, pod length were main positive contributing traits towards second component. Ten pod weight, pod width were positively contributing component towards principal component third. Pods per plant and shelling percentage were main traits contributing to principal component-IV, where as plant height, pod length and pod width were major positively contributing traits towards principal component -V. PS-1100, Meethi Phali, PB-87 and FC-1 were most divergent genotypes. On the basis of cluster mean of single linkage cluster analysis, Custer-I was best for number of primary branches and shelling percentage, Cluster -II for plant height,10 pods weight and pod yield per plant, cluster-III for number of pods /plants and cluster IV for pod length. Selection of genotypes from divergent clusters and components having more than one positive traits for hybridization programme may lead to improvement in yield and quality of pea.

Cluster Analysis of Gallstone FT-IR Spectra: Tests on Simulated Mixture Spectra and Comparison between Spectral and Morphological Classification of Human Gallstones

1998 ◽  
Vol 52 (9) ◽  
pp. 1210-1221 ◽  
Author(s):  
Eric Laloum ◽  
Nguyen Quy Dao ◽  
Michel Daudon
Keyword(s):  
Cluster Analysis ◽  
Ir Spectra ◽  
Black Pigment ◽  
Brown Pigment ◽  
Morphological Classification ◽  
Single Linkage ◽  
Homogeneous Groups ◽  
Ft Ir ◽  
Pure Cholesterol

Sixty-four combination spectra of three major gallstone components [i.e., cholesterol, calcium bilirubinate, and calcium carbonate (aragonite)] were simulated in accordance with a “fractal” ternary diagram. Comparison between the original pattern of composition and factorial maps of pretreated spectra makes it possible to show the effects of different normalization procedures (Euclidean norm, spectrum maximum, and area under spectrum set to 1). Cluster analysis of these spectra, depending on different agglomerative links (single linkage, complete linkage, average linkage, and Ward's criterion), was carried out. All the resultant trees yield the same groups, but Ward's criterion best preserves the pattern of the data. More than 100 gallstones from France and Vietnam were classified by using cluster analysis of their FT-IR spectra with Ward's criterion. Seven homogeneous groups of spectra were extracted, which have been significantly correlated to the four morphological types of gallstones: pure cholesterol, mixed cholesterol, brown pigment, and black pigment stones. This analysis also reveals that the morphological groups are not homogeneous in composition, in particular for black pigment stones.

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