Diversity Assessment of Indian Sunnhemp (Crotalaria juncea L.) Accessions for Enhanced Biomass and Fibre Yield using Geographic Information System Approach

2021 ◽  
Author(s):  
R.T. Maruthi ◽  
A. Anil Kumar ◽  
S.B. Choudhary ◽  
H.K. Sharma ◽  
J. Mitra
Keyword(s):  
Cluster Analysis ◽  
Germplasm Collection ◽  
Hierarchical Cluster ◽  
High Biomass ◽  
Lignocellulosic Substrate ◽  
Phenotypic Data ◽  
Diversity Pattern ◽  
Diversity Assessment ◽  
Agglomerative Hierarchical Cluster Analysis ◽  
Fibre Yield

Background: Sunnhemp, a rapid growing, high biomass yielding bast fibre crop has a tremendous potentiality in biofuels sector as a lignocellulosic substrate. In order to capitalize the new found area there is a need to identify high biomass and fibre yielding sunnhemp genotypes. The present study provides details of morphological diversity and geographical distribution pattern of Indian sunnhemp accessions. Methods: A total of 42 germplasm accessions collected from ten different states were evaluated for fibre yield and attributing traits in April-June cropping season. Based on phenotypic data agglomerative hierarchical cluster analysis was performed. Geographical coordinates of germplasm collection site were utilized to derive the spatial genetic diversity pattern for green biomass yield and fibre yield.Result: Phenotypic evaluation revealed significant genetic variability among the genotypes for biomass and fibre yield leading to identification of several promising accessions. Cluster analysis and PCA grouped the 42 sunnhemp accessions into three clusters. Cluster II and III are highly divergent harboring contrasting phenotypes. DIVA-GIS approach identified eastern Rajasthan, western Jharkhand and border area between Bihar and Jharkhand as sites of highest sunnhemp diversity. 

scholarly journals Use of cluster analysis to monitor novel coronavirus-19 infections in Maharashtra, India

2020 ◽  
Vol 72 ◽  
pp. 44-48 ◽  
Author(s):  
Sanjay Kumar
Keyword(s):  
Cluster Analysis ◽  
Hierarchical Cluster ◽  
Healthcare Management ◽  
The State ◽  
Optimum Number ◽  
Box Plots ◽  
Agglomerative Hierarchical Cluster Analysis ◽  
Medical Facilities ◽  
Novel Coronavirus ◽  
Almost All

Objectives: A novel coronavirus disease (COVID-19) has been continuously spreading in almost all the districts of the state Maharashtra in India. As a part of the healthcare management development, it is very important to monitor districts affected due to novel coronavirus (COVID-19). The main objective of this study was to identify and classify affected districts into real clusters on the basis of observations of similarities within a cluster and dissimilarities among different clusters so that government policies, decisions, medical facilities (ventilators, testing kits, masks, treatment etc.), etc. could be improved for reducing the number of infected and deceased persons and hence cured cased could be increased. Material and Methods: In the study, we focused on COVID-19 affected districts of the state Maharashtra of India. We applied agglomerative hierarchical cluster analysis, one of data mining techniques to fulfill the objective. Elbow method was used for obtaining an optimum number of clusters for further analysis. The study of variations among various clusters for each of the variables was performed using box plots. Results: Results obtained from the Elbow method suggested three optimum numbers of clusters for each of the variables. For confirmed and cured cases, cluster I corresponded to the districts BI, GO, ND, PA, SI, WS, JN, CH, OS, HI, NB, JG, RT, LA, KO, AM, ST, BU, DH, AK, YTL, SN, AH, SO, AU, RG, NG, NS and PL. Cluster II corresponded to the districts TH and PU and cluster III corresponded to the district MC. For the death cases, cluster I corresponded to the districts BI, GO, ND, PA, SI, WS, JN, CH, OS, HI, NB, JG, RT, LA, KO, AM, ST, BU, DH, AK, YTL, SN, AH, SO, AU, RG, NG, NS, PL and TH. Cluster II corresponded to the district PU and cluster III corresponded to the district MC. Conclusions: The study showed that the district MC under cluster III was affected severely with COVID-19 which had high number of confirmed cases. A good percentage of cured cases were found in some of the districts under cluster I where six districts (GO, SI, CH, OS, SN) had 100% success rate to cure patients. It was observed that the districts TH, PU and MC under clusters II and III had severe conditions which need optimization of medical facilities and monitoring techniques like screening, closedown, curfews, lockdown, evacuations, legal actions, etc.

scholarly journals Geostatistical Analysis of Groundwater Data

2019 ◽  
Vol 9 (1) ◽  
pp. 2734-2741
Keyword(s):  
Cluster Analysis ◽  
Statistical Analysis ◽  
Hierarchical Cluster ◽  
Geostatistical Analysis ◽  
Water Requirements ◽  
Groundwater Levels ◽  
Groundwater Level Fluctuations ◽  
Agglomerative Hierarchical Cluster Analysis ◽  
Observation Wells ◽  
Surface And Groundwater

Water resources are stressed because of the country's increasing population and increased water requirements. Even though a good understanding of both surface and groundwater hydrological systems make it possible to manage these resources properly. To study the main characteristics of formation of clusters of groundwater levels, statistical analysis has been used. Geostatistics is a class of statistics used to analyze and predict the values associated with spatial or spatiotemporal phenomena. It incorporates the spatial (and in some cases temporal) coordinates of the data within the analyses. The Statistical analysis is applied to monthly groundwater levels fluctuation data over a period of 2004-2017 in Mysuru, Mandya, Chamarajanagara and Hassan districts of Southern Karnataka in India. The groundwater levels data is collected from 197 Observation Wells from the districts. The Statistical methods like K-Means Clustering and Agglomerative Hierarchical Cluster Analysis is used to perform the datasets. Grouping is made using AHC method, during this process results are obtained by graph called Dendrogram. The obtained results are compared with the LULC maps of all 4 districts. Different grouping (cluster) is made for groundwater level fluctuations for proper conclusion to arrive.

scholarly journals A COMPARISON BETWEEN SINGLE LINKAGE AND COMPLETE LINKAGE IN AGGLOMERATIVE HIERARCHICAL CLUSTER ANALYSIS FOR IDENTIFYING TOURISTS SEGMENTS

2012 ◽  
Vol 12 (6) ◽  
Author(s):  
Noor Rashidah Rashid
Keyword(s):  
Cluster Analysis ◽  
Hierarchical Cluster Analysis ◽  
Hierarchical Cluster ◽  
Single Linkage ◽  
Complete Linkage ◽  
Multivariate Method ◽  
Agglomerative Hierarchical Cluster ◽  
Linkage Methods ◽  
Agglomerative Hierarchical Cluster Analysis ◽  
Analyze Data

Cluster Analysis is a multivariate method in statistics. Agglomerative Hierarchical Cluster Analysis is one of approaches in Cluster Analysis. There are two linkage methods in Agglomerative Hierarchical Cluster Analysis which are Single Linkage and Complete Linkage. The purpose of this study is to compare between Single Linkage and Complete Linkage in Agglomerative Hierarchical Cluster Analysis. The comparison of performances between these linkage methods was shown by using Kruskal-Wallis test. The result of the comparison used for segmenting tourists of Kapas Island. The statistical software SPSS has been applied to analyze data of this research. The result from Kruskal-Wallis test shows Complete Linkage is more useful in identifying tourists segments. Keywords : Agglomerative Hierarchical Cluster Analysis, Single Linkage, Complete Linkage, Kruskal-Wallis test, tourists

scholarly journals Implications of COVID-19 vaccination and public health countermeasures on SARS-CoV-2 variants of concern in Canada: evidence from a spatial hierarchical cluster analysis

2021 ◽  
Author(s):  
Daniel A Adeyinka ◽  
Cheryl Camillo ◽  
Wendie Marks ◽  
Nazeem Muhajarine
Keyword(s):  
Public Health ◽  
Cluster Analysis ◽  
Hierarchical Cluster Analysis ◽  
Vaccine Coverage ◽  
Hierarchical Cluster ◽  
Vaccine Uptake ◽  
Mobility Index ◽  
Agglomerative Hierarchical Cluster Analysis ◽  
Mitigating Factors ◽  
Central Canada

Background: The influence of coronavirus disease-2019 (COVID-19) containment measures on variants of concern (VOC) has been understudied in Canada. Our objective was to identify provinces with disproportionate prevalence of VOC relative to COVID-19 mitigation efforts in provinces and territories in Canada. Methods: We analyzed publicly available provincial- and territorial-level data on the prevalence of VOCs in relation to mitigating factors (summarized in three measures: 1. strength of public health countermeasures: stringency index, 2. how much people moved about outside their homes: mobility index, and 3. vaccine intervention: proportion of Canadian population fully vaccinated). Using spatial agglomerative hierarchical cluster analysis (unsupervised machine learning), the provinces and territories were grouped into clusters by stringency index, mobility index and full vaccine coverage. Kruskal-Wallis test was used to determine the differences in the prevalence of VOC (Alpha, or B.1.1.7, Beta, or B.1.351, Gamma, or P.1, and Delta, or B.1.617.2 variants) between the clusters. Results: Three clusters of vaccine uptake and countermeasures were identified. Cluster 1 consisted of the three Canadian territories, and characterized by higher degree of vaccine deployment and lesser degree of countermeasures. Cluster 2 (located in Central Canada and Atlantic region) was typified by lesser implementation of vaccine deployment and moderate countermeasures. The third cluster was formed by provinces inthe Pacific region, Central Canada, and Prairie region, with moderate vaccine deployment but stronger countermeasures. The overall and variant-specific prevalence were significantly different across the clusters. Interpretation: This study found that implementation of COVID-19 public health measures varied across the provinces and territories. Considering the high prevalence of VOCs in Canada, completing the second dose of COVID-19 vaccine in a timely manner is crucial.

scholarly journals Distribution and Taxonomic Significance of Secondary Metabolites Occurring in the Methanol Extracts of the Stonecrops (Sedum L., Crassulaceae) from the Central Balkan Peninsula

2015 ◽  
Vol 10 (6) ◽  
pp. 1934578X1501000
Author(s):  
Gordana S. Stojanović ◽  
Snežana Č. Jovanović ◽  
Bojan K. Zlatković
Keyword(s):  
Cluster Analysis ◽  
Chemical Composition ◽  
Secondary Metabolites ◽  
Hplc Analysis ◽  
Methanol Extract ◽  
Hierarchical Cluster ◽  
Balkan Peninsula ◽  
Taxonomic Significance ◽  
Methanol Extracts ◽  
Agglomerative Hierarchical Cluster Analysis

The present study is engaged in the chemical composition of methanol extracts of Sedum taxa from the central part of the Balkan Peninsula, and representatives from other genera of Crassulaceae ( Crassula, Echeveria and Kalanchoe) considered as out-groups. The chemical composition of extracts was determined by HPLC analysis, according to retention time of standards and characteristic absorption spectra of components. Identified components were considered as original variables with possible chemotaxonomic significance. Relationships of examined plant samples were investigated by agglomerative hierarchical cluster analysis (AHC). The obtained results showed how the distribution of methanol extract components (mostly phenolics) affected grouping of the examined samples. The obtained clustering showed satisfactory grouping of the examined samples, among which some representatives of the Sedum series, Rupestria and Magellensia, are the most remote. The out-group samples were not clearly singled out with regard to Sedum samples as expected; this especially applies to samples of Crassula ovata and Echeveria lilacina, while Kalanchoe daigremontiana was more separated from most of the Sedum samples.

DRSA: a non-hierarchical clustering algorithm using k-NN graph and its application in vegetation classification

2015 ◽  
pp. 125-138 ◽  
Author(s):  
I. V. Goncharenko
Keyword(s):  
Cluster Analysis ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Clustering Algorithms ◽  
Protein Structures ◽  
Hierarchical Cluster ◽  
Vegetation Classification ◽  
K Nearest Neighbor ◽  
Neighbor Graph ◽  
Nearest Neighbor Graph

In this article we proposed a new method of non-hierarchical cluster analysis using k-nearest-neighbor graph and discussed it with respect to vegetation classification. The method of k-nearest neighbor (k-NN) classification was originally developed in 1951 (Fix, Hodges, 1951). Later a term “k-NN graph” and a few algorithms of k-NN clustering appeared (Cover, Hart, 1967; Brito et al., 1997). In biology k-NN is used in analysis of protein structures and genome sequences. Most of k-NN clustering algorithms build «excessive» graph firstly, so called hypergraph, and then truncate it to subgraphs, just partitioning and coarsening hypergraph. We developed other strategy, the “upward” clustering in forming (assembling consequentially) one cluster after the other. Until today graph-based cluster analysis has not been considered concerning classification of vegetation datasets.

Sign in / Sign up

Export Citation Format

Share Document