Article
Cluster analysis of binary data is a relatively poorly developed task in comparison with cluster analysis for data measured on stronger scales. For example, at the stage of variable selection one can use many methods arranged for arbitrary measurement scales but the results are usually of poor quality. In practice, the only methods dedicated for variable selection for binary data are the ones proposed by Brusco (2004), Dash et al. (2000) and Talavera (2000). In this paper the efficiency of
Jerzy Korzeniewski
Statistics in Transition New Series , ISSUE 2, 295–304
Research Article
and cluster analysis has been applied to classify the stations. The factors have been classified into 4 components by the method of principal components. The stations have been classified into 6 groups using hierarchical cluster analysis. The methods, Average linkage between group and Within-groupslinkage, and distance-type measures Euclidean distance and Squared Euclidean distance were compared to verify the results of cluster analysis. The grouping of the stations has
Svetla STOILOVA,
Radina NIKOLOVA
Transport Problems , ISSUE 2, 143–155
Research Article
, density of the route. The new and the existing indicators have been used to analyze and classify the metro networks. The statistical method cluster analysis has been applied to classify the networks. Ten indicators have been used to carry out an analysis. The metro systems in European capitals have been classified in three clusters. The first cluster includes large metro systems, the second one includes small metro networks whereas the third cluster includes metro networks with only one line. The
Svetla STOILOVA,
Veselin STOEV
Transport Problems , ISSUE 2, 35–48
Research paper
The article addresses the measurement and identification problems covering particular social and economic areas (referred to as functions) in the regions of the country, based on the employment structure analysis and assessment by the sectors of the economy. The Herfindahl-Hirschman index was applied to measure sectoral concentration and Florence’s coefficient of localization to determine regional functional specialization. Finally, cluster analysis was conducted to produce the functional
Marek Obrębalski,
Marek Walesiak
Statistics in Transition New Series , ISSUE 2, 223–242
Article
Numerical taxonomy was used for identification and grouping of the genera, species, and populations in the families Merliniidae and Telotylenchidae. The variability of each of 44 morphometric characters was evaluated by calculation of the coefficient of variability (CV) and the ratio of extremes (max/min) in the range of 1,020 measured females. Also correlation and regression analyses were made between characters to find potential collinearities. Hierarchical cluster analysis (HCA
REZA GHADERI,
HABIBALLAH HAMZEHZARGHANI,
AKBAR KAREGAR
Journal of Nematology , ISSUE 2, 207–222
Research Article
The study deals with enhancing the reliability of freight cars by improving the corporate service system. Assessing of the quality of spare parts suppliers is discussed. An algorithm for supplier selection and an evaluation method, based on cluster analysis of indicators of supplier reliability, is proposed. Alternative developments for a service network, in view of expanding of the car fleet powered by natural gas-based fuel have been considered.АннотацияВ статье рассматриваются способы
Irina Makarova,
Rifat Khabibullin,
Eduard Belyaev,
Larisa Gabsalikhova,
Eduard Mukhametdinov
Transport Problems , ISSUE 1, 5–18
Article
: cluster analysis, k-means method, generalised distance measure GDM and interval taxonomic method TMI. The analysis was performed on the basis of HETUS data.
Marta Hozer-Koćmiel,
Christian Lis
Statistics in Transition New Series , ISSUE 2, 317–330
Article
identified. The considerations included in Table 3 reduce this number to 10 normalization methods. Next, the article discusses the procedure which allows separating groups of normalization methods leading to similar rankings of the set of objects separately for each composite indicator formula. The proposal, based on Kendall’s tau correlation coefficient (Kendall, 1955) and cluster analysis, can reduce the problem of choosing the normalization method. Based on the suggested research procedure the
Marek Walesiak
Statistics in Transition New Series , ISSUE 4, 693–710
Article
identified. The considerations included in Table 3 reduce this number to 10 normalization methods. Next, the article discusses the procedure which allows separating groups of normalization methods leading to similar rankings of the set of objects separately for each composite indicator formula. The proposal, based on Kendall’s tau correlation coefficient (Kendall, 1955) and cluster analysis, can reduce the problem of choosing the normalization method. Based on the suggested research procedure the
Marek Walesiak
Statistics in Transition New Series , ISSUE 4, 693–710
Article
Hough transformation. Then, the global structure parameters are acquired through cluster analysis. Secondly, the grayscale image is divided into several detection regions (each of which includes one intersection point to be detected) in accordance to the obtained global structure parameters and the intersection points in the detection regions are accurately located using the ridge line fitting method. Finally, the intersection points in the left and right images are matched based on their
L. M. Yang,
A. H. Zhang,
D. M. Lin,
L. Zhu
International Journal on Smart Sensing and Intelligent Systems , ISSUE 1, 256–273
Article
Elżbieta Roszko-Wójtowicz,
Jacek Białek
Statistics in Transition New Series , ISSUE 1, 167–180
Original Paper
Actinobacteria and Proteobacteria but a shift towards Proteobacteria was observed with increasing arsenic concentration, and number of Actinobacteria eventually decreases. PCA (Principle Component Analysis) plot of bacterial community composition indicated a distinct resemblance among high arsenic content samples, while low arsenic content samples remained separated from others. Cluster analysis of soil parameters identifies three clusters, each of them was related to the arsenic content. Further, cluster
Semanti Basu,
Tanima Paul,
Priya Yadav,
Abhijit Debnath,
Keka Sarkar
Polish Journal of Microbiology , ISSUE 2, 209–221
Research Article
Wojciech Łukaszonek
Statistics in Transition New Series , ISSUE 2, 271–290
original-paper
BO SHU,
JING YING,
TAO WANG,
MENGQIAN XIA,
WENYU ZHAO,
LING YOU
Polish Journal of Microbiology , ISSUE 1, 83–92
research-article
Scutellonema truncatum (accession number: KX959308) were selected for COI mtDNA data set.
Cluster analysis and web-based key
The new species was compared with 103 described species, based on the tabular key of Castillo and Vovlas (2005). This was done using Hierarchical Cluster analysis implemented in the software Primer 6 (Clarke and Gorley, 2006) using Bray–Curtis similarity measure with the percent similarity between species defined by the average of the multiple characters. The 11 characters of 103
Huu Tien Nguyen,
Quang Phap Trinh,
Marjolein Couvreur,
Phougeishangbam Rolish Singh,
Wilfrida Decraemer,
Wim Bert
Journal of Nematology , 1–20
original-paper
sanguinis (AJ251778.1)
95
aBands are numbered according to Fig. 1.
bIdentity represents the sequence identity (%) compared with that in the GenBank database.
Multivariate analysis of DGGE profiles. UPGMA cluster analysis was used to examine relationships among the four Daqus (Yunita et al. 2018). Comparisons by the UPGMA cluster analysis (Fig. 1B) using the Dice correlation coefficient showed that Wuling Daqu and Deshan Daqu grouped first, and then clustered with Baisha Daqu and Niulanshan Daqu
YUXI LING,
WENYING LI,
TONG TONG,
ZUMING LI,
QIAN LI,
ZHIHUI BAI,
GUIJUN WANG,
JIAHAO CHEN,
YUGUANG WANG
Polish Journal of Microbiology , ISSUE 1, 27–37
Article
Jana Cibulková,
Zdenek Šulc,
Sergej Sirota,
Hana Rezanková
Statistics in Transition New Series , ISSUE 2, 33–47