scholarly journals Structure and Dynamics of Fish Assemblages in a Tidal Creek Environment

2002 ◽  
Vol 45 (3) ◽  
pp. 365-373 ◽  
Author(s):  
Ana Lúcia Vendel ◽  
Henry Louis Spach ◽  
Sabine Granado Lopes ◽  
César Santos
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Community Structure ◽  
Tidal Creek ◽  
Fish Assemblages ◽  
Principal Component ◽  
Seasonal Patterns ◽  
Qualitative And Quantitative ◽  
Structure And Dynamics

Studies were carried out on structure and dynamics of fish assemblages in the Baguaçu tidal creek, Paranaguá Bay, Brazil. A total of 30,104 fish were captured, comprising 21 families and 47 species. Both in weight and in number, the species Anchoa parva prevailed. Monthly captures in number and weight were largest in the autumn and part of the winter. No seasonal tendency was observed in the indexes of community structure. The dendrogram produced by the classification of the samples separated the 12 months of collection into three groups, reflecting differences in the qualitative and quantitative occurrences of the most important taxa. Some ecological likeness, not only seasonal patterns of abundance, seemed evident in the seasonality of the groupings of species revealed through the cluster analysis. The principal component analysis reflected mainly the periods of rain and drought

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.

scholarly journals Multivariate analysis of genetic diversity in Phytophthora pod rot resistant exotic cocoa germplasm

2020 ◽  
pp. 202-212
Author(s):  
S.A. Veeresh ◽  
J.S. Minimol ◽  
B. Suma ◽  
P.S. Panchami ◽  
K.S. Shilpa
Keyword(s):  
Genetic Diversity ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Research Work ◽  
Principal Component ◽  
High Variability ◽  
Qualitative And Quantitative ◽  
Quantitative Characters ◽  
Eigen Value ◽  
Hybridization Programme

The current research work was carried out to evaluate the genetic diversity associated with thirty cocoa accessions resistant to Phytophthora. The cluster analysis and principal component analysis evaluated the genetic variability among the different genotypes. The highest number of genotypes were observed in cluster III (8) when qualitative traits were considered. In quantitative cluster analysis, most of the genotypes were placed in separate clusters due to high variability in the germplasm. Principal component (PC) analysis showed that the first three PCs with more than one Eigen-value contributed to 79.9 per cent of variability for different traits. When qualitative and quantitative characters were considered along with resistant reaction, clusters with genotypes highly resistant to Phytophthora pod rot were observed. Hybridization programme involving these resistant hybrids belonging to diverse clusters will result in high yielding hybrids with ample resistance.

scholarly journals Chemometric characterization and classification of new wheat genotypes

2021 ◽  
Vol 9 (1) ◽  
pp. 101-106
Author(s):  
Filip Kraic ◽  
Ján Mocák ◽  
Tibor Roháčik ◽  
Jana Sokolovičová
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Principal Component ◽  
High Food ◽  
Linear Discriminant ◽  
Wheat Genotypes ◽  
Standard Set ◽  
Is Development ◽  
Production Characteristics

The final goal of this work is development of new genotypes of wheat with better properties compared to the standard set. Prediction of optimal descriptors and properties related to the production characteristics is performed using several statistical and chemometrical tools, like correlation analysis, principal component analysis, cluster analysis and linear discriminant analysis. Optimisation of wheat genotypes is directed towards high food quality.

scholarly journals Discrimination and classification of tobacco wastes by identification and quantification of polyphenols with LC-MS/MS

2010 ◽  
Vol 75 (7) ◽  
pp. 875-891 ◽  
Author(s):  
Jun Wang ◽  
Dingqiang Lu ◽  
Hui Zhao ◽  
Ben Jiang ◽  
Jiali Wang ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Chemical Composition ◽  
Active Compound ◽  
Methanolic Extract ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Chlorogenic Acids ◽  
Tobacco Waste

The chemical composition of polyphenols in tobacco waste was identified by HPLC-PAD-ESI/MS/MS and the contents of chlorogenic acids and rutin in 10 varieties of tobacco wastes were determined by HPLC-UV. The relationships between the contents of active polyphenols and the varieties of tobacco wastes were interpreted by hierarchical cluster analysis (HCA) and principal component analysis (PCA). The results showed that 15 polyphenols were identified in a methanolic extract of dried tobacco waste. The tobacco wastes were characterized by high levels of chlorogenic acids (3-CQA, 5-CQA, and 4-CQA) and rutin; their ranges in the 10 tobacco varieties were 0.116-0.196, 0.686-1.781, 0.094- 0.192, and 0.413-0.998 %, respectively. According to multivariate statistics models, two active compound variables can be considered important for the discrimination of the varieties of tobacco wastes: chlorogenic acids and rutin. Consequently, samples of 10 tobacco varieties were characterized into three groups by HCA based on the PCA pattern. In conclusion, tobacco waste could be used as a new pharmaceutical material for the production of natural chlorogenic acids and rutin in the ethnopharmacological industry.

scholarly journals CLASSIFICATION OF LIDAR DATA OVER BUILDING ROOFS USING K-MEANS AND PRINCIPAL COMPONENT ANALYSIS

2018 ◽  
Vol 24 (1) ◽  
pp. 69-84
Author(s):  
Renato César dos Santos ◽  
Mauricio Galo ◽  
Vilma Mayumi Tachibana
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Real Data ◽  
Component Analysis ◽  
Lidar Data ◽  
Qualitative And Quantitative ◽  
Quantitative Analyses ◽  
Geometric Primitives ◽  
Different Levels

Abstract: The classification is an important step in the extraction of geometric primitives from LiDAR data. Normally, it is applied for the identification of points sampled on geometric primitives of interest. In the literature there are several studies that have explored the use of eigenvalues to classify LiDAR points into different classes or structures, such as corner, edge, and plane. However, in some works the classes are defined considering an ideal geometry, which can be affected by the inadequate sampling and/or by the presence of noise when using real data. To overcome this limitation, in this paper is proposed the use of metrics based on eigenvalues and the k-means method to carry out the classification. So, the concept of principal component analysis is used to obtain the eigenvalues and the derived metrics, while the k-means is applied to cluster the roof points in two classes: edge and non-edge. To evaluate the proposed method four test areas with different levels of complexity were selected. From the qualitative and quantitative analyses, it could be concluded that the proposed classification procedure gave satisfactory results, resulting in completeness and correctness above 92% for the non-edge class, and between 61% to 98% for the edge class.

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