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Journal articles on the topic 'Cluster analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Ahamad, Mohammed Gulam, Mohammed Faisal Ahmed, and Mohammed Yousuf Uddin. "Clustering as Data Mining Technique in Risk Factors Analysis of Diabetes, Hypertension and Obesity." European Journal of Engineering and Technology Research 1, no. 6 (2018): 88–93. http://dx.doi.org/10.24018/ejeng.2016.1.6.202.

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This investigation explores data mining using open source software WEKA in health care application. The cluster analysis technique is utilized to study the effects of diabetes, obesity and hypertension from the database obtained from Virginia school of Medicine. The simple k-means cluster techniques are adopted to form ten clusters which are clearly discernible to distinguish the differences among the risk factors such as diabetes, obesity and hypertension. Cluster formation was tried by trial and error method and also kept the SSE as low as possible. The SSE is low when numbers of clusters ar

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D. Shashibhushan, C. V. Sameer Kumar, and Ravi Kiran Reddy Kondi. "Genetic diversity analysis of Pearl Millet germplasm by cluster analysis." emergent Life Sciences Research 08, no. 01 (2022): 70–74. http://dx.doi.org/10.31783/elsr.2022.817074.

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There are 40 genotypes of pearl millet that were gathered from ICRISAT in Hyderabad for the study. The experiment was performed in triplicate using Randomized Block Design. With an average D2 value of 398.08, the experimental material was divided into seven clusters, indicating the presence of diversity across the lines for the attributes being studied. Among different clusters the maximum cluster lines i.e., ten lines were observed in cluster V followed by cluster IV, cluster III, cluster I, cluster II, cluster VII, and cluster VI. Cluster V has the greatest intra cluster distance, followed b

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Klyuchko, O. M. "CLUSTER ANALYSIS IN BIOTECHNOLOGY." Biotechnologia Acta 10, no. 5 (2017): 5–18. http://dx.doi.org/10.15407/biotech10.05.005.

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KUMAWAT, ANAND. "Genetic divergence analysis of soybean (Glycine Max L.) genotypes using mahalanobis multivariate analysis." Annals of Plant and Soil Research 26, no. 1 (2024): 172–74. http://dx.doi.org/10.47815/apsr.2024.10348.

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Wilk's criterion was used to group the 25 genotypes into three different clusters based on the D2 values. Cluster III was the biggest with 11 genotypes, followed by cluster II with 8 genotypes and cluster I with 6 genotypes. Cluster mean was the highest for days to maturity in cluster II (92.96) and lowest for number of primary branches/plant in cluster III (3.45). Using cluster means genetic diversity analysis reveals genetic backgrounds and interactions of germplasm and manages crop primary pools. The highest inter-cluster distance was observed between cluster II and I, followed by cluster I

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Godara, Poonam, Shrawan Kumar, and Darvinder Kumar. "Evaluation of Genetic Variation in Indian mustard (Brassica Juncea L Czern and Coss) Using Multivariate Techniques." Journal of Agriculture Research and Technology 47, no. 03 (2022): 344–48. http://dx.doi.org/10.56228/jart.2022.47315.

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A set of 310 lines of Indian mustard (Brassica juncea L Czern and Coss) were analysed for cluster and principal component analysis (PCA). PCA identified four principal components which explained 65.13% of total variability among the 310 genotypes. Hierarchical cluster analysis grouped 310 genotypes into 3 clusters. Cluster1 included maximum number of 155 genotypes and clusters 3 had the lowest number of 43 genotypes. The grouping pattern of genotypes obtained by cluster analysis and PCA plots was almost similar.

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Zhang, Xiaoli, Qiang Liu, Shengyang Niu, et al. "Varietal Differences Among the Fruit Quality Characteristic of 15 Spine Grapes (Vitis davidii Foëx)." HortScience 57, no. 10 (2022): 1282–88. http://dx.doi.org/10.21273/hortsci16702-22.

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Spine grape (Vitis davidii Foëx), an important wild grape species in South China, has gained attention because of its health-promoting effects and use in the wine industry. Fruit quality plays an important role in determining the quality of wine; however, a suitable evaluation system to monitor its fruit quality has not been established. The fruit quality characteristics (phenolics and aromas) of 15 spine grapes grown in China were evaluated using a combination of principal component and cluster analyses. The total sugar, organic acid, and phenolic content ranged from 81.80 to 154.89 mg·g−1, 8

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Frontera, Jennifer A., Lorna E. Thorpe, Naomi M. Simon, et al. "Post-acute sequelae of COVID-19 symptom phenotypes and therapeutic strategies: A prospective, observational study." PLOS ONE 17, no. 9 (2022): e0275274. http://dx.doi.org/10.1371/journal.pone.0275274.

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Background Post-acute sequelae of COVID-19 (PASC) includes a heterogeneous group of patients with variable symptomatology, who may respond to different therapeutic interventions. Identifying phenotypes of PASC and therapeutic strategies for different subgroups would be a major step forward in management. Methods In a prospective cohort study of patients hospitalized with COVID-19, 12-month symptoms and quantitative outcome metrics were collected. Unsupervised hierarchical cluster analyses were performed to identify patients with: (1) similar symptoms lasting ≥4 weeks after acute SARS-CoV-2 inf

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Kundu, Anirban, Guanxiong Xu, and Chunlin Ji. "Analysis on Cloud Classification using Accessibility." International Journal of Cloud Applications and Computing 4, no. 3 (2014): 44–53. http://dx.doi.org/10.4018/ijcac.2014070103.

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In this paper, Cloud classification has been demonstrated using accessibility factor of distinct Cloud clusters. Group and non-group Cloud structures have been classified using its direction of scope of activities. Each type of Cloud is further divided into different clusters based on its unique status, such as reachable cluster, non-reachable cluster, basin cluster, momentary cluster, and initiation cluster. Set theory has been applied to realize our proposed Cloud system.

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Talekar, S. C., M. Vani Praveena, and R. G. Satish. "Genetic diversity using principal component analysis and hierarchical cluster analysis in rice." INTERNATIONAL JOURNAL OF PLANT SCIENCES 17, no. 2 (2022): 191–96. http://dx.doi.org/10.15740/has/ijps/17.2/191-196.

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A set of 100 germplasm lines with four checks viz., BPT-5204, PSB-68, Siri1253 and MGD-101 were evaluated in augmented block design during Kharif 2020. The observations were documented for 5 quantitative traits viz., days to 50% flowering, panicle length, number of panicles per square meter, 1000 grain weight and grain yield by principal component analysis and cluster analysis to determine the relationship and genetic divergence among the individuals. The cumulative variance of 55.60% was explained by 1st two principal components (PC1 and PC2) with eigen values greater than 1. Component 1 with

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Al Jauhar, Hafizh Syihabuddin, Solimun Solimun, and Rahma Fitriani. "Integration of DBSCAN Cluster Analysis with Multigroup Moderation Path Analysis." CAUCHY: Jurnal Matematika Murni dan Aplikasi 10, no. 1 (2025): 72–81. https://doi.org/10.18860/cauchy.v10i1.29847.

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This study examines the application of integration between DBSCAN cluster analysis and multigroup moderation path analysis to analyse patterns of waste management behaviour in Batu City. DBSCAN was used to cluster the data based on density, resulting in two main clusters as well as some noise data. The first cluster consisted of 189 respondents, while the second cluster included 196 respondents, with the remaining 10 data identified as noise. The DBSCAN clustering results showed a silhouette index of 0.664, indicating good clustering quality in terms of compactness and separation between clust

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Singh, Jay, C. L. Maurya, Rishabh Gupta, et al. "Genetic Divergence Analysis of Wheat (Triticum aestivum L.) Genotypes." Journal of Experimental Agriculture International 46, no. 5 (2024): 287–92. http://dx.doi.org/10.9734/jeai/2024/v46i52377.

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A laboratory experiment was carried out with 30 indigenous genotypes of bread wheat along with three checks in a complete randomized design for divergence analysis. The trial was conducted at Seed Technology laboratory of Acharya Narendra Deva University of Agriculture and Technology Kumarganj, Ayodhya during 2020-21. The observations were recorded on thirteen-character Test weight (1000-grain weight), seed width (mm), seed length (mm), shoot length, root length, seedling length, seedling dry weight, germination (%), first count, final count, vigour index-I, vigour index-II. The 30 genotypes g

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Amin, MF, M. Hasan, NCD Barma, MG Rasul, and MM Rahman. "Genetic diversity analysis in spring wheat (Triricum aestivum L.)." Bangladesh Journal of Agricultural Research 39, no. 2 (2014): 189–96. http://dx.doi.org/10.3329/bjar.v39i2.20414.

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Genetic divergences of 50 wheat lines were studied through Mohalanobiss D2 and principal component analysis for fourteen characters. Genotypes were grouped into four different clusters. Cluster II comprised maximum number of genotypes (twenty one) followed by cluster IV. The inter-cluster distance was maximum between clusters I and III (12.29) indicating wide genetic diversity between these two clusters followed by the distance between cluster I and cluster II (8.28), and cluster III and cluster IV (7.97). The minimum inter-cluster distance was observed between cluster II and cluster IV (4.19

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Khan, Nazmul Alam, Mohammad Saiful Islam, Saikat Hossain Bhuiyan, K. M. Mehedi Hasan, and Mustafa Kamrul Hasan. "Evaluation of yield contributing characters and cluster analysis of soybean genotypes." Algerian Journal of Biosciences 3, no. 1 (2022): 027–32. http://dx.doi.org/10.57056/ajb.v3i1.52.

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A morphological divergence study among the twenty genotypes based on nine yield and yield contributing characters through the D2 statistic indicated the presence of substantial diversity by forming clusters with a wide range of inter-cluster distances. The soybean genotypes under investigation were divided into five clusters. Cluster I had the most genotypes, with 10, followed by clusters III and V, each with five and three genotypes. The relative divergence indicates how much each cluster varies from the others. Cluster I and Cluster III have the most significant order of divergence, followed

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Dudhatra, R. S., Y. A. Viradiya, K. B. Joshi, T. A. Desai, and G. K. Vaghela. "Genetic divergence analysis in Groundnut (Arachis hypogaea L.) genotypes." emergent Life Sciences Research 08, no. 01 (2022): 114–18. http://dx.doi.org/10.31783/elsr.2022.81114118.

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The Mahalanobis D2 statistic was utilized to quantify genetic dissimilarity among groundnut genotypes for thirteen features. Tocher's approach was used to divide genotypes into groups. In all, ten clusters were established from 40 genotypes. Cluster I has sixteen genotypes subsequently cluster II has eight, clusters IV and VI contain three, cluster III contains five, and clusters V, VII, VIII, IX, and X had a single genotype. This classification revealed a greater amount of variation among genotypes. Cluster II had a desirable rating for the number of pods per plant. The cluster IΧ had a desir

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S. K. Sinha, V. Netam, K. Tigga V. K. Singh, and N. Chouksey. "Genetic Diversity Analysis by D2 Analysis in Fine Scented Genotypes of Rice (Oryza sativa L.)." International Journal of Current Microbiology and Applied Sciences 10, no. 11 (2021): 48–55. http://dx.doi.org/10.20546/ijcmas.2021.1011.007.

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The present investigation on “Diversity analysis by D2 analysis in fine scented genotypes of rice (Oryza sativa L.)” was used to investigate the diversity among 40 fine scented genotypes obtained from the Indira Gandhi Krishi Vishwavidyalaya in Raipur. The current studies was conducted at research cum instructional farm, IGKV, RMD Ambikapur, Chhattisgarh. The experiment was conducted in RBD with purpose to characterized 40 genotypes of rice along with 4 checks viz. CG Sugandhitbhog, CG Devbhog, Indira Sugandhit Dhan-1 and Dubrajsel 1 for diversity. Based on cluster analysis, the genotypes were

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P, Akshatha, Y. C. Vishwanath, Vijayakumar B. Narayanapur, Sarvamangala S. Cholin, Ramangouda Hadlageri, and Bapurayagowda Bagavantagouda Patil. "Morphological and Molecular Diversity Analysis in Fennel." Journal of Advances in Biology & Biotechnology 28, no. 1 (2025): 915–24. https://doi.org/10.9734/jabb/2025/v28i11947.

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A study was conducted to assess the morphological and molecular diversity of fennel using 20 genotypes. Multivariate analysis based on morphological traits grouped all genotypes into five major clusters. Cluster II was having nine genotypes, cluster I was having seven genotypes and cluster III was having 2 genotypes out of total genotypes used in this study. The other clusters were mono-genotypic. The highest inter-cluster distance was found between Clusters III and IV. Moreover, cluster IV was superior based on the cluster means for maximum yield-related traits. For the molecular analysis, 11

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Saidov, Mukhammadali, and Ilhom Ochilov. "Theoretical analysis of agricultural clusters in innovative economy." BIO Web of Conferences 65 (2023): 03006. http://dx.doi.org/10.1051/bioconf/20236503006.

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This article analyzes the theoretical aspects of clusters in the innovative economy and their management, including the increase in economic efficiency of agro-clusters as a result of the organization of the agricultural sector on the basis of “production - service - production - storage - supply - processing - sales” development of cluster services, increase in production and diversification of exported finished products as a result of improvements, the authors have developed a unique, new definition of the term “cluster”. From a methodological point of view, the “face” of a cluster, its spec

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Altomare, Michele, Andrea Chierici, Francesco Virdis, et al. "Centralization of Major Trauma Influences Liver Availability for Transplantation in Northern Italy: Lesson Learned from COVID-19 Pandemic." Journal of Clinical Medicine 11, no. 13 (2022): 3658. http://dx.doi.org/10.3390/jcm11133658.

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Background: During the COVID-19 pandemic, the centralization of patients allowed trauma and transplants referral centers to continue their routine activity, ensuring the best access to health care. This study aims to analyze how the centralization of trauma is linked with liver allocation in Northern Italy. Methods: Cluster analysis was performed to generate patient phenotype according to trauma-related variables. Comparison between clusters was performed to evaluate differences in damage control strategy procedures (DCS) performed and the 30-day graft dysfunction. Results: During the pandemic

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Jatothu, Jawahar Lal, A. Anil Kumar, S. B. Choudhary, et al. "Genetic diversity analysis in tossa jute (Corchorus olitorius L.) germplasm lines." Journal of Applied and Natural Science 10, no. 1 (2018): 1–3. http://dx.doi.org/10.31018/jans.v10i1.1566.

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An experiment was conducted to examine the magnitude of genetic diversity and characters contributing to genetic diversity among 81 tossa jute genotypes. Cluster analysis based on Euclidean squared distances and ward’s method, the genotypes were grouped into ten distinct clusters. Analysis reveals that stick weight contributes maximum to the phenotypic diversity (65.52%) followed by green weight (13.64%) and fibre yield (10.10%). Among the clusters Cluster IX recorded highest mean fibre yield (19.91g) followed by Cluster VII (18.94g) and these clusters also recorded high mean values for plant

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Kumar, Indraneel, Lionel J. Beaulieu, Andrey Zhalnin, and Chun Song. "Occupational Competitiveness Analysis of the U.S. Transportation and Logistics Cluster." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 1 (2020): 249–59. http://dx.doi.org/10.1177/0361198120901677.

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This paper explores occupational or human capital attributes of transportation and logistics clusters in the U.S., by analyzing logistics clusters, such as Memphis and uncovers the differences in occupations or knowledge and skill contents of the workforce. The research builds on previous studies funded by the U.S. Economic Development Administration on U.S. occupation clusters providing insights on logistics clusters from a human capital perspective. The study draws specifically from the industry-and-occupation cluster crosswalks building on previous research on occupation cluster industry cl

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Shrestha, Jiban. "Cluster Analysis of Maize Inbred Lines." Journal of Nepal Agricultural Research Council 2 (December 30, 2016): 33–36. http://dx.doi.org/10.3126/jnarc.v2i0.16119.

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The determination of diversity among inbred lines is important for heterosis breeding. Sixty maize inbred lines were evaluated for their eight agro morphological traits during winter season of 2011 to analyze their genetic diversity. Clustering was done by average linkage method. The inbred lines were grouped into six clusters. Inbred lines grouped into Clusters II had taller plants with maximum number of leaves. The cluster III was characterized with shorter plants with minimum number of leaves. The inbred lines categorized into cluster V had early flowering whereas the group into cluster VI

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Staszewska, Jolanta. "CLUSTER DEVELOPMENT – INTERNATIONAL ANALYSIS OF AUTOMOTIVE CLUSTERS." Zeszyty Naukowe Wyższej Szkoły Humanitas Zarządzanie 20, no. 2 (2019): 25–35. http://dx.doi.org/10.5604/01.3001.0013.5207.

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Cluster issues are the subject of many considerations and analyzes. This article presents the content referring to functioning automotive clusters in the Śląskie Voivodeship in Poland and Slovakia. The aim of the articles is presentation and comparative analysis of two automotive clusters operating in different countries and a comparison of their development opportunities through SWOT / TOWS analysis. The article presents general information related to the cluster concept, characteristics of clusters in Slovakia and Poland and results of network comparisons taking into account the strategic ap

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Riasna, Iryna. "Fuzzy Cluster Analysis: Pseudometrics and Fuzzy Clusters." Cybernetics and Computer Technologies, no. 1 (April 28, 2023): 23–34. http://dx.doi.org/10.34229/2707-451x.23.1.3.

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Introduction. Clustering problems arise in various spheres of human activity. In cases where there are no initial data sufficient for statistical analysis or information obtained from experts is used, fuzzy models are proposed that take into account different types of uncertainty and more argumentatively reflect real situations that model systems of different purposes. Particular attention is drawn to invariance in problems with different types of data measured in different scales according to the classification of S. Stevens. It is known that when solving cluster analysis problems using the t

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Mondal, Chandan Kumar, Pinaki Acharyya, and Uttam Saha. "Study on genetic diversity in chilli (Capsicum annuum) based on multivariate analysis and isozyme analysis." Journal of Applied and Natural Science 8, no. 4 (2016): 1884–92. http://dx.doi.org/10.31018/jans.v8i4.1057.

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Thirty seven diverse chilli (Capsicum annuum) genotypes were studied for 22 growth, yield and fruit quality traits. Multivariate analysis grouped the genotypes into 7 clusters. Cluster IV was largest containing 14 genotypes. Inter cluster distance was maximum between cluster V and VII (248.09), and minimum between cluster I and II (57.80). Cluster VII was most heterogeneous (intra-cluster divergence value 191.25) and Cluster II was most homogeneous (30.25). Genotypes were also analyzed for peroxidase enzyme polymorphism using gel electrophoresis which resulted seven electrophoretic bands (Rf 0

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Baisya, Ritasman, Phani Kumar Devarasetti, Murthy G. S. R., and Liza Rajasekhar. "Autoantibody Clustering in Systemic Lupus Erythematosus–Associated Pulmonary Arterial Hypertension." Indian Journal of Cardiovascular Disease in Women - WINCARS 06, no. 02 (2021): 100–105. http://dx.doi.org/10.1055/s-0041-1732510.

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AbstractSystemic lupus erythematous–associated pulmonary arterial hypertension (SLE-PAH) is one of the important causes of mortality in lupus patients. Different autoantibodies are associated with SLE-PAH which can predict its future development. The objective of the study was to identify distinct autoantibody-based clusters in SLE-PAH patients and to compare demographic characters, clinical phenotypes, and therapeutic strategy across the clusters. Three distinct autoantibody clusters were identified using k-means cluster analysis in 71 SLE-PAH patients. Cluster1 had predominant Sm-RNP, Smith,

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Ara, N., M. Moniruzzaman, Ferdousi Begum, M. Moniruzzaman, and R. Khatoon. "Genetic divergence analysis in papaya (Carica papaya L.) Genotypes." Bangladesh Journal of Agricultural Research 41, no. 4 (2016): 647–56. http://dx.doi.org/10.3329/bjar.v41i4.30697.

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The experiment on papaya (Carica papaya L.) consisting of fourteen genotypes from diversd gene pool was conducted at the Regional Agricultural Research Station, Ishurdi, Pabna during April 2013 to May 2014 to study the nature and magnitude of genetic divergence and eventually identification of suitable genotypes for use in breeding program. Multivariate analysis was subjected to assess the genetic diversity and Mahalanobis generalized distance (D2) was used to assess the divergence present among the genotypes. The fourteen genotypes were grouped into four clusters. The cluster IV had the maxi

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Girardi, Marisa. "Optical Analysis of Cluster Mergers." Highlights of Astronomy 12 (2002): 510–12. http://dx.doi.org/10.1017/s1539299600014222.

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AbstractAn increasing amount of data has revealed that many clusters are very complex systems. Optical analyses show that several clusters contain subsystems of galaxies suggesting that they are still in the phase of relaxation, possibly after a phase of cluster merging. I briefly review the main results about substructure, and the connection between cluster dynamical status and galaxy properties. Useful comparisons with the results derived from X-ray data are also discussed.

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M. Mampana, Kgwadi, Solly M. Seeletse, and Enoch M. Sithole. "Optimized consortium formation through cluster analysis." Problems and Perspectives in Management 14, no. 1 (2016): 117–26. http://dx.doi.org/10.21511/ppm.14(1).2016.13.

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Some problems cannot be solved optimally and compromises become necessary. In some cases obtaining an optimal solution may require combining algorithms and iterations. This often occurs when the problem is complex and a single procedure does not reach optimality. This paper shows a conglomerate of algorithms iterated in tasks to form an optimal consortium using cluster analysis. Hierarchical methods and distance measures lead the process. Few companies are desirable in optimal consortium formation. However, this study shows that optimization cannot be predetermined based on a specific fixed nu

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Kim, Eunjung, Yumi Won, and Jieun Shin. "Analysis of Children’s Physical Characteristics Based on Clustering Analysis." Children 8, no. 6 (2021): 485. http://dx.doi.org/10.3390/children8060485.

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This study assessed the physical development, physical fitness (muscular endurance, muscular strength, flexibility, agility, power, balance), and basal metabolic rate (BMR) in a total of 4410 children aged six (73–84 months) residing in Korea. Their physical fitness was visually classified according to the physical fitness factor and—considering that children showed great variations in the physical fitness criteria depending on their physique and body composition—the study aimed to assess characteristics such as physique and BMR, the precursor for fat-free mass, based on the physical health cl

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Кыдырбаева, И. "CLUSTER FORMATION POTENTIAL ANALYSIS." Vestnik Bishkek state university af. K. Karasaev 1, no. 59 (2022): 3–5. http://dx.doi.org/10.35254/bhu/2022.59.3.

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This article high lights the concept of the cluster and the advantages of the cluster approach, as well as in order to increase the competitiveness of the regional economy, it is necessary to determine an effective model of the regional economy that allows the maximum use of the existing potential. Cluster policy is one of the most common mechanisms for the implementation of state and regional economic policy. The main goal of implementing the cluster policy is to ensure high rates of economic growth and diversification of the economy by increasing the competitiveness of enterprises, suppliers

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Hasan, MJ, Umma Kulsum, MMH Rahman, MMH Chowdhury, and AZMKA Chowdhury. "Genetic diversity analysis of parental lines for hybrid development in rice (Oryza sativa L.)." Bangladesh Journal of Agricultural Research 37, no. 4 (2013): 617–24. http://dx.doi.org/10.3329/bjar.v37i4.14386.

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Genetic divergence of 40 parental lines comprising 30 restorer and 10 maintainer lines were studied through Mohalanobis's D2 and principal component analysis for eleven characters. Genotypes were grouped into five different clusters. Cluster V comprised maximum number of genotypes (thirteen) followed by cluster I and II. The inter-cluster distance was maximum between clusters I and V (13.495) indicating wide genetic diversity between these two clusters followed by the distance between cluster I and 11 (9.489), cluster IV, and cluster V (8.969) and cluster I and cluster III (8.039). The minimum

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Meena, Vijay Kumar, Preeti Verma, Rajdeep Jajoriya, and Ritu Sharma. "Genetic Diversity Analysis in Chickpea (Cicer arietinum L.) Genotypes in South-Eastern Rajasthan, India." Journal of Experimental Agriculture International 47, no. 4 (2025): 16–22. https://doi.org/10.9734/jeai/2025/v47i43353.

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A field experiment was conducted during Rabi 2019-20 to study genetic diversity in forty chickpea genotypes. The grouping of chickpea genotypes into eight clusters suggested the presence of wide genetic diversity among them. The Cluster III was the largest including 10 genotypes followed by cluster IV (9 genotypes), cluster II (7 genotypes), cluster VI (6 genotypes), cluster V (4 genotypes), cluster VI (2 genotypes) while clusters I and VIII had one genotype each. The maximum inter cluster distance was observed between cluster V and cluster VIII, followed by cluster I and VIII, cluster VI and

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Nikitina, M. A., I. M. Chernukha, Ya M. Uzakov, and D. E. Nurmukhanbetova. "CLUSTER ANALYSIS FOR DATABASES TYPOLOGIZATION CHARACTERISTICS." Series of Geology and Technical Sciences 2, no. 446 (2021): 114–21. http://dx.doi.org/10.32014/2021.2518-170x.42.

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The article deals with basic concepts of cluster analysis and data clustering. The authors give brief information on the history of cluster analysis and its first applications. The article gives the classification of methods by the way of data processing and analysis in cluster analysis. The detailed description of the popular, non- hierarchical K-means algorithm is given. When developing databases, their structure should provide for the division of products into clusters based on various characteristics. It is necessary to consider the division into clusters based on other characteristics, su

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Jahan, N., SR Bhuiyan, MZA Talukder, MA Alam, and M. Parvin. "Genetic Diversity Analysis In Brassica rapa Using Morphological Characters." Bangladesh Journal of Agricultural Research 38, no. 1 (2013): 11–18. http://dx.doi.org/10.3329/bjar.v38i1.15185.

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A field experiment was conducted in the experimental field of Genetics and Plant Breeding Department, Sher-e Bangla Agricultural University, Dhaka, Bangladesh to study on genetic diversity in 10 F4 lines obtained through intervarietal crosses along with 8 released varieties of Brassica rapa during November 2007 to February 2008. Different Multivariate analyses were performed to classify 18 genotypes. All the genotypes were grouped into four clusters. Cluster IV was the largest comprising of 7 genotypes and cluster II was the smallest with 2 genotypes. Cluster II had the highest intra-cluster d

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Capra, Miranda G. "Factor Analysis of Card Sort Data: An Alternative to Hierarchical Cluster Analysis." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 49, no. 5 (2005): 691–95. http://dx.doi.org/10.1177/154193120504900512.

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Software and product designers use card sorting to understand item groups and relationships. In the usability community, a common method of formal statistical analysis for open card sort data is hierarchical cluster analysis, which results in a tree of the items sorted into distinct, nested clusters. Hierarchical cluster analysis is appropriate for highly structured settings, like software menus. However, many situations call for softer clusters, such as designing websites where multiple pages link to the same target page. Factor analysis summarizes the categories created in card sorts and gen

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Kraska, Ewa. "Analysis of the Functioning of Clusters in Poland." Journal of International Business Research and Marketing 7, no. 1 (2021): 29–33. http://dx.doi.org/10.18775/jibrm.1849-8558.2015.71.3004.

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The cluster concept has gained special significance after the publication of the works of M. Porter “The Competitive Advantage of Nations”(1990). But the most popular definition of industrial cluster was formed eight years later, when he wrote that clusters are:“geographic concentrations of interconnected companies, specialized suppliers, service providers, firms in related industries, and associated institutions (e.g. universities, standards agencies, trade associations) in a particular field that compete but also cooperate”(Porter, 1998, p. 197). A cluster as a regionally focused form of eco

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Zaman, MA, MNA Siddquie, M. Mahbubur Rahman, MY Abida, and MJ Islam. "Divergence analysis of drought tolerant Genotypes of Wheat (Triticum astivum L.)." Bangladesh Journal of Agricultural Research 39, no. 3 (2015): 385–96. http://dx.doi.org/10.3329/bjar.v39i3.21982.

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Thirty genotypes of wheat were grown in an Alpha Lattice Design with three replications for evaluation and divergence analysis. Seeds were sown on 24 November 2011 at Regional Wheat Research Centre, Bangladesh Agricultural Research Institute, Shyampur, Rajshahi. Significant variation was observed among the genotypes and these are grouped into six clusters. Clusters III and VI were comprised of maximum number of genotypes (6) followed by clusters I, IV, and V with 5 genotypes and the minimum genotypes (3) were in cluster II. The maximum inter-cluster distance was recorded between the Cluster VI

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Zhang, Dawei, Fuding Xie, Dapeng Wang, Yong Zhang, and Yan Sun. "Cluster Analysis Based on Bipartite Network." Mathematical Problems in Engineering 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/676427.

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Clustering data has a wide range of applications and has attracted considerable attention in data mining and artificial intelligence. However it is difficult to find a set of clusters that best fits natural partitions without any class information. In this paper, a method for detecting the optimal cluster number is proposed. The optimal cluster number can be obtained by the proposal, while partitioning the data into clusters by FCM (Fuzzyc-means) algorithm. It overcomes the drawback of FCM algorithm which needs to define the cluster numbercin advance. The method works by converting the fuzzy c

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Chandana, Bandela, Adheena Ram A., Seeja G., Surendran M., and Susha S. Thara. "Genetic Diversity Analysis of Rice (Oryza sativa L.) Genotypes for Yield and Sheath Blight Screening." Journal of Advances in Biology & Biotechnology 27, no. 11 (2024): 413–20. http://dx.doi.org/10.9734/jabb/2024/v27i111626.

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Sheath blight disease, caused by Rhizoctonia solani Kühn, is considered the second most important disease affecting rice, causing yield reduction globally. In the present study thirty diverse rice genotypes were inoculated with fungal mycelia during the maximum tillering stage to assess the genetic diversity of rice genotypes for sheath blight reactions. Mahalanobis D2 statistics was performed for grain yield and yield contributing features under sheath blight stress to calculate the genetic divergence between the genotypes. Four clusters were formed from the genotypes. Cluster III has the hig

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Wang, Hongyong, Xiaohao Xu, and Yifei Zhao. "Empirical analysis of aircraft clusters in air traffic situation networks." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 231, no. 9 (2016): 1718–31. http://dx.doi.org/10.1177/0954410016660870.

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The existing research on air traffic complexity ignores the effects of air traffic situation structure and, thus, cannot reflect the heterogeneous traffic density distribution in airspace. In this study, the structure of air traffic situation was characterized using the idea of community structure in complex networks. An aircraft cluster model was built, and an aircraft cluster discovery method based on depth-first traversal was proposed. The aircraft cluster division effect was comprehensively represented by cluster performance indices, including cohesion and stability. The routinely recorded

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V. A., Mohanlal, and K. Saravanan. "Exploring genetic diversity in traditional rice genotypes (Oryza sativa L.) through cluster analysis of qualitative and quantitative traits." Ecology, Environment and Conservation 30, no. 02 (2024): 494–98. http://dx.doi.org/10.53550/eec.2024.v30i02.011.

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Fifty-three traditional rice genotypes (Oryza sativa L.) were characterized by following the 41 DUS descriptors, which includes 29 qualitative and 12 quantitative traits. The data used for qualitative and quantitative cluster analysis. In qualitative and quantitative cluster analysis, the genotypes grouped into six clusters. Both qualitative and quantitative clustering analyses revealed distinct patterns of genetic diversity among the traditional rice genotypes. The genotype, G31 grouped in cluster VI in both cluster analyses. In quantitative cluster analysis, cluster III possessed genotypes w

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Srivastava, Mitali, Manojkumar HG, and Atar Singh. "Analysis of Genetic Diversity in Greengram (Vigna radiata L. Wilczek)." Journal of Experimental Agriculture International 46, no. 4 (2024): 1–7. http://dx.doi.org/10.9734/jeai/2024/v46i42334.

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The current study titled “Analysis of genetic diversity in green gram [Vigna radiata (L.) Wilczek]” was carried out at Center for Crop Research (C.R.C.), Sardar Vallabhbhai Patel University of Agriculture & Technology, Meerut. A set of twenty five mungbean genotypes were examined to investigate the essence and extend of divergence of genes using Mahalanobis’s D2 Statistics on twelve critical quantitative attributes. The study material was assessed using Randomized Block Design (RBD) with three replication plots of two rows of 4 meter length. Out of every replication, five plants were selec

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Wang, Juan, and Kwang Sook Sung. "A Study on the Discourse and Characteristics of Design Expression of NFT Virtual Fashion Design through China Social Media Big date Analysis." Korea Institute of Design Research Society 8, no. 2 (2023): 335–52. http://dx.doi.org/10.46248/kidrs.2023.2.335.

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This study aims to analyze the discourse and design characteristics of ‘NFT virtual fashion’ through social media big data analysis. Data was collected using the keyword ‘NFT virtual fashion’ from Textom China, and the discourse of NFT virtual fashion was analyzed using CONCOR analysis. Cluster analysis was employed to examine the design characteristics of NFT virtual fashion. Four discourse groups emerged from the CONCOR analysis: Group 1 discussed the trends in NFT virtual fashion brands and its development, Group 2 explored the experiences and creation of NFT virtual fashion, Group 3 examin

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Akter, F., MZ Islam, A. Akter, SK Debsharma, A. Shama, and M. Khatun. "Genetic Diversity of Bacterial Blight Resistant Rice (Oryza sativa L.) Genotypes from INGER." Bangladesh Rice Journal 23, no. 2 (2020): 59–64. http://dx.doi.org/10.3329/brj.v23i2.48248.

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Genetic diversity of 65 rice genotypes was studied from IRBBN (International Rice Bacterial Blight Nursery) of INGER (International Network for Genetic Evaluation of Rice) materials through Mahalanobis D2 statistic for grain yield and yield contributing characters. The genotypes were grouped into five clusters. The inter-cluster distances were higher than intra-cluster distances indicating wider genetic diversity among the genotypes of different clusters. The intra-cluster distances were lower in all the cases reflecting homogeneity of the genotypes within the clusters. The cluster III contain

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Kohberger, Robert C., and Brian S. Everitt. "Cluster Analysis." Technometrics 36, no. 2 (1994): 216. http://dx.doi.org/10.2307/1270235.

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D'Agostino, Ralph B., and Brian Everitt. "Cluster Analysis." Journal of the American Statistical Association 89, no. 425 (1994): 359. http://dx.doi.org/10.2307/2291241.

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Arnold, Gillian M. "Cluster Analysis." Journal of the Royal Statistical Society: Series D (The Statistician) 52, no. 3 (2003): 407–8. http://dx.doi.org/10.1111/1467-9884.00369_8.

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Saunders, John. "Cluster analysis." Journal of Marketing Management 10, no. 1-3 (1994): 13–28. http://dx.doi.org/10.1080/0267257x.1994.9964257.

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Yang, Shengping, and Gilbert Berdine. "Cluster analysis." Southwest Respiratory and Critical Care Chronicles 6, no. 26 (2018): 37–40. http://dx.doi.org/10.12746/swrccc.v6i26.504.

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Bonner, R. E. "CLUSTER ANALYSIS." Annals of the New York Academy of Sciences 128, no. 3 (2006): 972–83. http://dx.doi.org/10.1111/j.1749-6632.1965.tb11711.x.

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