scholarly journals Occupational Disease as the Bane of Workers’ Lives: A Study of Its Incidence in Slovakia. Part 2

2021 ◽  
Vol 18 (24) ◽  
pp. 12990
Author(s):  
Miriam Andrejiova ◽  
Miriama Pinosova ◽  
Miroslav Badida
Keyword(s):  
Cluster Analysis ◽  
Occupational Diseases ◽  
Principal Component ◽  
Working Environment ◽  
Physical Factors ◽  
Cophenetic Correlation ◽  
Average Linkage ◽  
Linkage Method ◽  
Noise Vibration ◽  
New Variables

The main objective of this article is to monitor the development of the number of occupational diseases related to selected physical factors in the working environment (noise, vibration and dust). Each region of Slovakia has its own specific social and economic conditions. Due to the existence of a strong correlation between the several regional variables observed, principal component analysis (PCA) was used to determine the new variables. Cluster analysis was used to group regions with similar characteristics. A dendrogram was created using the average linkage method, which illustrated the similarity of the regions studied. The value of the cophenetic correlation coefficient (CC = 0.90) confirms the validity of the average linkage method. The result of the cluster analysis is the grouping of the eight regions into five homogenic groups (clusters). An analysis of the data shows that Slovakia’s regional differences significantly influence the incidence of occupational diseases in individual regions. It is shown that, in Slovakia, the development of the number of occupational diseases has seen a favourable trend in the long term.

scholarly journals THE COMPARISON OF TRANSPORT INFRASTRUCTURES IN INDIVIDUAL SLOVAK REGIONS BY APPLYING PCA AND CLUSTER ANALYSIS

2020 ◽  
Vol 7 (4) ◽  
pp. 225-234
Author(s):  
Miriam Andrejiova ◽  
Zuzana Kimakova
Keyword(s):  
Cluster Analysis ◽  
Statistical Methods ◽  
Principal Component ◽  
Final Analysis ◽  
Hierarchical Cluster ◽  
Transport Infrastructure ◽  
Cophenetic Correlation ◽  
Average Linkage ◽  
Linkage Method ◽  
Input Variables

The development of the transport segment is currently an essential process which affects several other industries. The transport infrastructure and the services provided in this sector influence economic growth, the efforts aimed at increasing competitiveness, as well as prosperity of the society. One of the key problems Slovakia is facing is the long-term growth of differences between individual regions. The present article deals with the evaluation and comparison of selected transport infrastructure indicators in eight regions of Slovakia. The evaluation was carried out by applying basic statistical methods and multiple-criteria statistical methods. Every region was characterised by 20 selected variables describing its uniqueness (e.g. population, area, GDP per capita, road infrastructure etc.). The evaluation of similarities between individual regions in terms of selected variables was carried out by applying the Principal Component Analysis (PCA) and Hierarchical Cluster Analysis. Within the PCA, the original input variables were replaced with three principal components describing as much as 86.68% of the cumulative variance. The average linkage method, as one of the hierarchical methods, was applied to create a dendrogram representing the similarities between the regions of Slovakia. The cophenetic correlation coefficient value of CC=0.936 confirmed the proper selection of the average linkage method. The output of the cluster analysis was that 8 regions of Slovakia were divided into five similar homogenous clusters based on the examined variables. The final analysis indicated that the transport infrastructure and the development thereof significantly affect the differences between individual regions of Slovakia and, as a matter of fact, they belong to the factors creating such differences.

scholarly journals ANALISIS KLASTER METODE WARD DAN AVERAGE LINKAGE DENGAN VALIDASI DUNN INDEX DAN KOEFISIEN KORELASI COPHENETIC (Studi Kasus: Kecelakaan Lalu Lintas Berdasarkan Jenis Kendaraan Tiap Kabupaten/Kota di Jawa Tengah Tahun 2018)

2019 ◽  
Vol 8 (4) ◽  
pp. 486-495
Author(s):  
Sisca Indah Pratiwi ◽  
Tatik Widiharih ◽  
Arief Rachman Hakim
Keyword(s):  
Cluster Analysis ◽  
Euclidean Distance ◽  
Traffic Accidents ◽  
Optimal Number ◽  
Cophenetic Correlation ◽  
Central Java ◽  
Average Linkage ◽  
Linkage Method ◽  
Ward Method ◽  
Squared Euclidean Distance

Based on Central Java Regional Police data, traffic accidents from 2017 to 2018 increased from 17.522 to 19.016 or 8,54 percent. To reduce the number of traffic accidents in Central Java, the initial step was carried out by grouping districts/cities that had the same accident level characteristics based on vehicle type with cluster analysis. The ward and average linkage method is a hierarchical cluster analysis method. ward method can maximize cluster homogeneity. While the average linkage method can generate clusters with small cluster variants. In this study using a measure of squared euclidean distance to measure the similarity between pairs of objects. To determine the quality of clustering results, the validation dunn index and cophenetic coefficients corelation are used. Based on the results of the clustering, the optimal number of clusters is obtained at q = 5 for the average linkage method with the results of validation dunn index = 0,08571196 and the rcoph = 0,687458. Keywords: Accidents, Cluster Analysis, Ward Method, Average linkage, Squared Euclidean Distance, Dunn Index, Cophenetic Correlation Coefficient

scholarly journals Validity studies among hierarchical methods of cluster analysis using cophenetic correlation coefficient

2019 ◽  
Vol 7 (2A) ◽  
Author(s):  
Priscilla Ramos Carvalho ◽  
Casimiro Sepúlveda Munita ◽  
André Luiz Lapolli
Keyword(s):  
Cluster Analysis ◽  
Correlation Coefficient ◽  
Single Linkage ◽  
Cophenetic Correlation ◽  
Data Set ◽  
Archaeological Data ◽  
Average Linkage ◽  
Linkage Method ◽  
Cophenetic Correlation Coefficient ◽  
Statistical Program

The literature presents many methods for partitioning of data set, and is difficult choose which is the most suitable, since the various combinations of methods based on different measures of dissimilarity can lead to different patterns of grouping and false interpretations. Nevertheless, little effort has been expended in evaluating these methods empirically using an archaeological data set. In this way, the objective of this work is make a comparative study of the different cluster analysis methods and identify which is the most appropriate. For this, the study was carried out using a data set of 45 samples of ceramic fragments, analyzed by instrumental neutron activation analysis (INAA). The methods used for this study were: Single linkage, Complete linkage, Average linkage, Centroid and Ward. The validation was done using the cophenetic correlation coefficient and comparing these values the average linkage method obtained better results. A script of the statistical program R with some functions was created to obtain the cophenetic correlation. By means of these values was possible to choose the most appropriate method to be used in the data set.

scholarly journals Pengelompokkan Provinsi di Indonesia Berdasarkan PDRB atas Dasar Harga Konstan Tahun 2013

2017 ◽  
Vol 7 (2) ◽  
pp. 76
Author(s):  
Ferry Kondo Lembang ◽  
Patresya Yulita Lessil ◽  
Salmon Notje Aulele
Keyword(s):  
Cluster Analysis ◽  
Gross Domestic Product ◽  
Hierarchical Cluster Analysis ◽  
Hierarchical Cluster ◽  
Economic Conditions ◽  
Domestic Product ◽  
Economic Progress ◽  
Average Linkage ◽  
Linkage Method ◽  
Cluster 2

Regional gross domestic product is one of the important indicators to determine economic conditions in an area. Therefore it is very interesting to discuss and to determine the economic progress of a region. Cluster anlysis aims to classify objects based on the characteristics into cluster that have the properties that are relatively similar and clearly distinguish between one cluster agains another. The main objective of the research that classifies 33 provinces based on the value of regional gross domestic product at constant price in 2013 using hierarchical cluster analysis for average linkage method. The results showed that the cluster were carried out on 33 provinces in Indonesia formed 3 cluster with details of that cluster 1 consisting of Sumatera, Kalimantan, Sulawesi, Nusa Tenggara, Bali, Papua, Maluku and Jawa Tengah, DI Yogyakarta, and Banten, cluster 2 consisting of 1 provinces of DKI Jakarta and cluster 3 which consists of 2 provinces namely Jawa Barat dan Jawa Timur.

Molecular evidence supports the separation of Lentinula edodes from Lentinus and related genera

1992 ◽  
Vol 70 (12) ◽  
pp. 2446-2452 ◽  
Author(s):  
Francis I. Molina ◽  
Peng Shen ◽  
Shung-Chang Jong ◽  
Kazuhiko Orikono
Keyword(s):  
Cluster Analysis ◽  
Ribosomal Dna ◽  
18S Rdna ◽  
Lentinula Edodes ◽  
Molecular Evidence ◽  
Data Sets ◽  
Internal Transcribed Spacer Region ◽  
Cophenetic Correlation ◽  
Linkage Method ◽  
Restriction Polymorphisms

Restriction polymorphisms in two regions of the ribosomal DNA (rDNA) repeat unit were examined in 18 strains of Lentinus, Neolentinus, Pleurotus, and the shiitake mushroom Lentinula edodes. The polymerase chain reaction was used to separately amplify the 18S rDNA and the region spanning the two internal transcribed spacers and the 5.8S ribosomal RNA gene. Amplified products were digested with a battery of 10 restriction endonucleases and the two data sets were subjected to cluster analysis. All strains of Lentinula edodes consistently exhibited identical restriction profiles that were distinct from those of the genera Lentinus, Neolentinus, and Pleurotus. The internal transcribed spacer region exhibited more variability than the 18S rDNA, giving distinctive profiles for two strains of Lentinus tigrinus and for one strain of Neolentinus lepideus. Similarity coefficients were clustered with the unweighted pair group method with arithmetic average, single-linkage method, and complete-linkage method. Results from cluster analysis of the two data sets were highly congruent and tree topologies were consistent irrespective of the clustering method used. The distinctiveness of the groups was further confirmed by computing for consensus trees and cophenetic correlation coefficients. Ribosomal DNA restriction polymorphisms support the placement of the strains examined in separate taxa. Key words: Lentinula, Lentinus, Neolentinus, Pleurotus, ribosomal DNA.

scholarly journals CLUSTERING OF PROVINCE IN INDONESIA BASED ON AQUACULTURE PRODUCTIVITY USING AVERAGE LINKAGE METHOD

2021 ◽  
Vol 2 (1) ◽  
pp. 16-20
Author(s):  
Fachruddin Hari Anggara Putera ◽  
Septina F. Mangitung ◽  
Madinawati ◽  
Lilies Handayani
Keyword(s):  
Cluster Analysis ◽  
Standard Deviation ◽  
The State ◽  
Average Linkage ◽  
Linkage Method ◽  
Cluster 2 ◽  
Aquaculture Production

Fisheries are one of the agricultural sub-sectors that play an important role in contributing to income figures for the state and the region because most of Indonesia's territory is water so that the fisheries sector is a sub-sector that is feasible to be developed in this country, one of which is through aquaculture. One of the efforts that can increase and maintain productivity in the aquaculture sector is to classify provinces that produce aquaculture production into groups based on the similarity of characteristics possessed by each province in Indonesia. In this study, clustering was carried out using cluster analysis using the average linkage method and based on the analysis results obtained showed that cluster 1 consists of 25 provinces, cluster 2 consists of 5 provinces, cluster 3 consists of 2 provinces, cluster 4 consists of 1 province, and cluster 5 consists of 1 province with a standard deviation value within a cluster of 11,729 and a standard deviation between clusters of 118,745.

scholarly journals Study of the Percentage of Greenhouse Gas Emissions from Aviation in the EU-27 Countries by Applying Multiple-Criteria Statistical Methods

2020 ◽  
Vol 17 (11) ◽  
pp. 3759 ◽  
Author(s):  
Miriam Andrejiová ◽  
Anna Grincova ◽  
Daniela Marasová
Keyword(s):  
European Union ◽  
Cluster Analysis ◽  
Greenhouse Gas Emissions ◽  
Greenhouse Gas ◽  
Transport Sector ◽  
Gas Emissions ◽  
Member States ◽  
Average Linkage ◽  
Linkage Method ◽  
The Eu

The transport sector, including air transport, represents an important source of air pollution. The present article deals with the current situation regarding greenhouse gas emissions in the air in 27 European Union (EU-27) member states. Every member state is characterized by selected parameters that determine the unique nature of a particular country (e.g., population, area, life expectancy, gross domestic product (GDP) per capita, etc.). In addition to these parameters, there were also other parameters which were monitored as they characterize the amount of greenhouse gas emissions and the impact of aviation on these emissions. The main purpose of the article is to compare the European Union member states on the basis of 15 examined parameters. The identification of similarities between the EU-27 member states with regard to the selected parameters was carried out by applying principal component analysis (PCA) and hierarchical cluster analysis. The average linkage method was applied to create a dendrogram representing the similarities between the examined member states. The value of the cophenetic correlation coefficient CC = 0.923 confirmed the correct application of the average linkage method. The cluster analysis outputs were five similarity-based homogeneous groups (clusters) into which the 27 member states were divided on the basis of the examined variables.

scholarly journals Analisis Diskriminan Terhadap Pengelompokan Mutu Pendidikan Sekolah Menengah Atas di Kabupaten Lamongan

2020 ◽  
Vol 6 (2) ◽  
pp. 11-20
Author(s):  
Mu’tasim Billah ◽  
Novita Eka Chandra ◽  
Siti Amiroch
Keyword(s):  
Cluster Analysis ◽  
Factor Analysis ◽  
Discriminant Analysis ◽  
Principal Component ◽  
Original Data ◽  
Quality Of Education ◽  
Linkage Method ◽  
And Cluster Analysis ◽  
Index Value

Quality of education is the educational services ability that can fill the needs or expectations, satisfaction internally and externally which includes educational inputs, processes and outputs. The purpose of this reserach is to classify the quality of high school education in Lamongan District using factor, cluster and discriminant analysis. The dominant factors of 12 education quality variables can be known from the results of factor analysis using the PCA (Principal Component Analysis) method. The grouping of 48 high schools did by cluster analysis using 5 hierarchical methods. The validity index used to determine the optimal group number of the five hierarchical methods is RMSSTD (Root Mean Square Standard Deviation). The classification accuracy testing uses discriminant analysis based on the results of factor analysis and cluster analysis. Grouping the quality of education is influenced by dominant factors such as the number of classrooms, the value of accreditation, the number of certification and non-certification teachers, the number of education staff, the ratio of students to teachers, the number of laboratory rooms that can be known from the results of factor analysis. In cluster analysis, using the Mahalanobis distance because there is multicollinearity and the smallest RMSSTD index value obtained in the Complete Linkage method with 5 clusters. So, with discriminant analysis, it can be concluded that the grouping based on factor analysis and cluster analysis is 58.3% of the 48 processed data that has been entered in the group that matches the original data.

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.

Main approaches to prevention of health disorders in workers in contact with physical factors

2020 ◽  
Vol 60 (11) ◽  
pp. 827-829
Author(s):  
Andrey V. Melentyev
Keyword(s):  
Cardiovascular Risk ◽  
Cardiovascular Diseases ◽  
Machine Building ◽  
Working Environment ◽  
Physical Factors ◽  
Noise And Vibration ◽  
Industrial Enterprises ◽  
Increased Risk ◽  
Health Disorders ◽  
Building Enterprises

Introduction. One of the leading causes of occupational health loss, especially in mining and machine-building enterprises, is the combined impact of industrial noise and vibration. The wide prevalence of cardiovascular diseases is one of the most important medical and social problems, due to persistent disability and high mortality, bringing prevention of health disorders to the first place as the basis for preserving labor longevity. The aim of study is to identify the main approaches aimed at preventing health problems in workers who come into contact with vibration and noise at mining and machine-building enterprises. Materials and methods. A survey and survey of 296 industrial workers was conducted. Group 1 (160 people) included men who were exposed to noise and vibration factors above the maximum permissible levels, group 2 consisted of 136 men who did not have direct contact with noise and vibration generating equipment. When conducting an in-depth laboratory and instrumental examination in a hospital setting, all workers additionally calculated the level of cardiovascular risk on the SCORE scale. Statistical analysis was performed using the software package "Statistica 6.0". Results. It is determined that the priority adverse factors of the working environment in production are noise and vibration. It has been shown that individuals who come into contact with these factors are more likely to detect violations of lipid metabolism and endothelial function, have a higher average heart rate and systolic blood pressure, and have an increased risk of developing cardiovascular diseases. Conclusions. Taking into account the obtained results of the proposed diagnostic approaches aimed at the prevention of health disorders among workers of industrial enterprises. If employees are found to have an increased cardiovascular risk, it is necessary to conduct a more in-depth examination and timely medical and preventive measures.

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