scholarly journals CLASSIFICATION OF LOGISTICS-BASED TRANSPORTATION ACTIVITIES IN OECD COUNTRIES AND SELECTED NON-MEMBER COUNTRIES THROUGH CLUSTER ANALYSIS

2020 ◽  
pp. 478-501
Author(s):  
Gülsen Serap ÇEKEROL
Keyword(s):  
Cluster Analysis ◽  
Oecd Countries

Neurophysiologic classification of Wilson's disease by using cluster analysis

2006 ◽  
Vol 37 (01) ◽  
Author(s):  
W Hermann ◽  
T Villmann ◽  
HJ Kühn ◽  
P Baum ◽  
G Reichel ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Wilson’S Disease ◽  
Wilson's Disease

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.

A Classification of 148 U.S. Maize Inbreds: II. Validation of Cluster Analysis Based on RFLPs

Crop Science ◽  
1994 ◽  
Vol 34 (4) ◽  
pp. 852-865 ◽  
Author(s):  
Rita Hogan Mumm ◽  
Lawrence J. Hubert ◽  
J. W. Dudley
Keyword(s):  
Cluster Analysis ◽  
Maize Inbreds

Using clusterization principles for the selection of repair sections of main oil pipelines based on diagnostic data

2011 ◽  
Vol 8 (1) ◽  
pp. 201-210
Author(s):  
R.M. Bogdanov
Keyword(s):  
Cluster Analysis ◽  
Defect Density ◽  
Density Variation ◽  
Distance Functions ◽  
Oil Pipeline ◽  
Oil Pipelines ◽  
Classification Of Images ◽  
Main Oil Pipeline ◽  
Selection Of

The problem of determining the repair sections of the main oil pipeline is solved, basing on the classification of images using distance functions and the clustering principle, The criteria characterizing the cluster are determined by certain given values, based on a comparison with which the defect is assigned to a given cluster, procedures for the redistribution of defects in cluster zones are provided, and the cluster zones parameters are being changed. Calculations are demonstrating the range of defect density variation depending on pipeline sections and the universal capabilities of linear objects configuration with arbitrary density, provided by cluster analysis.

scholarly journals Structured Laser Illumination Planar Imaging Based Classification of Ground Coffee Using Multivariate Chemometric Analysis

2016 ◽  
Vol 8 (3) ◽  
pp. 32 ◽  
Author(s):  
Olivier K. Bagui ◽  
Kenneth A. Kaduki ◽  
Edouard Berrocal ◽  
Jeremie T. Zoueu
Keyword(s):  
Cluster Analysis ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Planar Imaging ◽  
Reliable Measurement ◽  
Laser Illumination ◽  
Coffee Beans ◽  
Chemometric Techniques ◽  
Ground Coffee

<p class="1Body">Most commercially available ground coffees are processed from Robusta or Arabica coffee beans. In this work, we report on the potential of Structured Laser Illumination Planar Imaging (SLIPI) technique for the classification of five types of Robusta and Arabica commercial ground coffee samples (Familial, Belier, Brazil, Colombia and Malaga). This classification is made, here, from the measurement of the extinction coefficient µ<sub>e</sub> and of the optical depth OD by means of SLIPI. The proposed technique offers the advantage of eliminating the light intensity from photons which have been multiply scattered in the coffee solution, leading to an accurate and reliable measurement of µ<sub>e</sub>. Data analysis uses the chemometric techniques of Principal Component Anaysis (PCA) for variable selection and Hierarchical Cluster Analysis (HCA) for classification. The chemometric model demonstrates the potential of this approach for practical assessment of coffee grades by correctly classifying the coffee samples according to their species.</p>

scholarly journals Metabolic Capacity Differentiates Plenodomus lingam from P. biglobosus Subclade ‘brassicae’, the Causal Agents of Phoma Leaf Spotting and Stem Canker of Oilseed Rape (Brassica napus) in Agricultural Ecosystems

Pathogens ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 50
Author(s):  
Magdalena Frąc ◽  
Joanna Kaczmarek ◽  
Małgorzata Jędryczka
Keyword(s):  
Cluster Analysis ◽  
Host Plant ◽  
Fungal Spores ◽  
Carbon Sources ◽  
Leptosphaeria Maculans ◽  
Stem Canker ◽  
Metabolic Capacity ◽  
Agricultural Ecosystems ◽  
Effective Growth

In contrast to the long-lasting taxonomic classification of Plenodomus lingam and P. biglobosus as one species, formerly termed Leptosphaeria maculans, both species form separate monophyletic groups, comprising sub-classes, differing considerably with epidemiology towards Brassicaceae plants. Considering the great differences between P. lingam and P. biglobosus, we hypothesized their metabolic capacities vary to a great extent. The experiment was done using the FF microplates (Biolog Inc., Hayward, CA, USA) containing 95 carbon sources and tetrazolium dye. The fungi P. lingam and P. biglobosus subclade ‘brassicae’ (3 isolates per group) were cultured on PDA medium for 6 weeks at 20 °C and then fungal spores were used as inoculum of microplates. The test was carried out in triplicate. We have demonstrated that substrate richness, calculated as the number of utilized substrates (measured at λ490 nm), and the number of substrates allowing effective growth of the isolates (λ750 nm), showed significant differences among tested species. The most efficient isolate of P. lingam utilized 36 carbon sources, whereas P. biglobosus utilized 60 substrates. Among them, 25–29 carbon sources for P. lingam and 34–48 substrates for P. biglobosus were efficiently used, allowing their growth. Cluster analysis based on Senath criteria divided P. biglobosus into two groups and P. lingam isolates formed one group (33% similarity). We deduce the similarities between the tested species help them coexist on the same host plant and the differences greatly contribute to their different lifestyles, with P. biglobosus being less specialized and P. lingam coevolving more strictly with the host plant.

scholarly journals EVALUATING THE DEGREE OF COMPLEXITY OF TIGHT OIL RECOVERY BASED ON THE CLASSIFICATION OF OILS

2020 ◽  
pp. 76-81
Author(s):  
G. Efendiyev ◽  
M. Karazhanova ◽  
D. Akhmetov ◽  
I. Piriverdiyev
Keyword(s):  
Cluster Analysis ◽  
Oil Recovery ◽  
Fuzzy Cluster ◽  
Fuzzy Cluster Analysis ◽  
Recovery Coefficient ◽  
Reservoir Permeability ◽  
Oil Density ◽  
The Relationship ◽  
Degree Of Recovery

The article discusses the results of the use of cluster analysis in assessing the degree of oil recovery complexity and its impact on the performance indicator. For this purpose, clustering was performed using a fuzzy cluster analysis algorithm. It should be noted that along with the deposits of heavy and highly viscous oils, a large share of hard-to-recover reserves is also confined to conditions with very low reservoir permeability values. Data on viscosity, oil density and oil permeability of in-situ conditions from various fields of Kazakhstan are collected. Using the results of this classification, a statistical analysis of indicators of various types of hard-torecover oils was performed. In the process of analysis, a generalized characteristic was determined for each class of oil, including viscosity, oil density and reservoir permeability. The generic characteristic is a linear transformation of the three characteristics. The results were subjected to statistical processing. At the same time, an attempt was made to establish and analyze the relationship between the degree of recovery complexity of hard-to-recover oils and oil recovery coefficient. In the course of the analysis, the average values of the oil recovery coefficient and the index of the degree of recovery complexity of hard-to-recover oil within each cluster were calculated and the relationship between them was plotted. The observed dependence, built on averaged points, is close to a power law, and, as one would expect, with an increase in the degree of oil recovery complexity, the oil recovery coefficient falls. The obtained estimates of the degree of oil recovery complexity allow us to rank different types of oils by their viscosity, density and reservoir permeability, which can be used to compare types of hard-to-recover oils by the value of the quality indicator. Methods to solve the problem of hard-to-remove high-viscosity and heavy oils should be aimed at reducing the viscosity of oil in the reservoir: injection of hot water / steam into the reservoir, the use of electric heaters, etc. Purpose. Assessment of the degree of oil recovery complexity and its impact on the efficiency of field development. The technique. The solution of the tasks set in the work was carried out on the method of mathematical statistics and the theory of fuzzy sets. In this case, the methods of processing the results, the correlation analysis, and the algorithm of fuzzy cluster analysis were used. Results. As a result of studies, 4 classes were obtained, each of which characterizes the degree of oil recovery complexity, a parameter was proposed for quantifying the degree of complexity, including oil density and viscosity, reservoir permeability, a relationship between this parameter and oil recovery coefficient was obtained. Scientific novelty. A classification of hard-to-recover reserves based on a fuzzy cluster analysis has been performed, and a parameter has been proposed for quantifying the degree of oil recovery complexity, a relationship has been obtained that allows judging the oil recovery by the degree of oil recovery complexity. Practical significance. The results obtained make it possible to classify hard-to-recover reserves and make decisions on the choice of methods for influencing the reservoir in various geological conditions.

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