Journal articles on the topic 'Cluster analysis'
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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 (July 27, 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 are more. Less than ten clusters formation unable to yield distinguishable information. In this work each cluster is revealing quit important information about the diabetes, obesity, hypertension and their interrelation. Cluster 0: Diabetes ? Obesity ? Hypertension = Healthy patient, Cluster 1: Diabetes ? Obesity ? Hypertension = Healthy patient, Cluster2: Diabetes ? Obesity ? Hypertension = Obesity, Cluster3: Diabetes ? Obesity ? Hypertension = Patients with Obesity and Hypertension, Cluster4: Boarder line Diabetes ? Obesity ? Hypertension = Sever obesity, Cluster5: Obesity ? Hyper tension ? Diabetes = Hypertension, Cluster6: Border line obese ? Border line hypertension ? Diabetes = No serious complications, Cluster 7: Obesity ? Hypertension ? Diabetes= Healthy patients, Cluster 8: Obesity ? Hypertension ? Diabetes= Healthy patients, and Cluster 9: Diabetes ? Hyper tension ? Obesity = High risk unhealthy patients.
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Klyuchko, O. M. "CLUSTER ANALYSIS IN BIOTECHNOLOGY." Biotechnologia Acta 10, no. 5 (October 2017): 5–18. http://dx.doi.org/10.15407/biotech10.05.005.
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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 by Cluster II, Cluster IV, and Cluster I. As a result, within these clusters, selection might be based on the greatest mean for desirable characteristics. The relative divergence shows the degree to which each cluster differs. The highest order of divergence was observed in cluster VIII and cluster X, followed by cluster V and cluster VIII. The results revealed that the parents are genetically heterogeneous in these clusters. The high heterotic response may have been achieved when used in a hybridization programme. Cluster VI and Cluster VII had the shortest inter-cluster distance, indicating low genetic diversity. Plant height had the highest cluster value in cluster VIII and the lowest in cluster X, whereas phenological parameters like days to flowering and days to maturity had the highest cluster value in cluster II. Days to flowering were the most important factor in genetic divergence, followed by the number of panicle length, fodder yield per plot, and productive tillers per plant.
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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 (February 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 III and II (3.142), and cluster III and I (2.913). This indicates wide diversity between genotypes in these clusters, which can be exploited to generate transgressive segregants. The highest intra-cluster distance was found for cluster I, followed by cluster II (1.961), and cluster III (1.913). It is suggested that genetic materials belonging to these clusters may be used as parents for hybridization programmes to develop desirable variety.
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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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Frontera, Jennifer A., Lorna E. Thorpe, Naomi M. Simon, Adam de Havenon, Shadi Yaghi, Sakinah B. Sabadia, Dixon Yang, et al. "Post-acute sequelae of COVID-19 symptom phenotypes and therapeutic strategies: A prospective, observational study." PLOS ONE 17, no. 9 (September 29, 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 infection, and (2) similar therapeutic interventions. Logistic regression analyses were used to evaluate the association of these symptom and therapy clusters with quantitative 12-month outcome metrics (modified Rankin Scale, Barthel Index, NIH NeuroQoL). Results Among 242 patients, 122 (50%) reported ≥1 PASC symptom (median 3, IQR 1–5) lasting a median of 12-months (range 1–15) post-COVID diagnosis. Cluster analysis generated three symptom groups: Cluster1 had few symptoms (most commonly headache); Cluster2 had many symptoms including high levels of anxiety and depression; and Cluster3 primarily included shortness of breath, headache and cognitive symptoms. Cluster1 received few therapeutic interventions (OR 2.6, 95% CI 1.1–5.9), Cluster2 received several interventions, including antidepressants, anti-anxiety medications and psychological therapy (OR 15.7, 95% CI 4.1–59.7) and Cluster3 primarily received physical and occupational therapy (OR 3.1, 95%CI 1.3–7.1). The most severely affected patients (Symptom Cluster 2) had higher rates of disability (worse modified Rankin scores), worse NeuroQoL measures of anxiety, depression, fatigue and sleep disorder, and a higher number of stressors (all P<0.05). 100% of those who received a treatment strategy that included psychiatric therapies reported symptom improvement, compared to 97% who received primarily physical/occupational therapy, and 83% who received few interventions (P = 0.042). Conclusions We identified three clinically relevant PASC symptom-based phenotypes, which received different therapeutic interventions with varying response rates. These data may be helpful in tailoring individual treatment programs.
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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 (July 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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Zhang, Xiaoli, Qiang Liu, Shengyang Niu, Chonghuai Liu, Xiucai Fan, Ying Zhang, Lei Sun, and Jianfu Jiang. "Varietal Differences Among the Fruit Quality Characteristic of 15 Spine Grapes (Vitis davidii Foëx)." HortScience 57, no. 10 (October 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, 8.02 to 15.48 mg·g−1, and 5.58 to 20.12 mg·g−1, respectively. The comprehensive assessment by principal component analysis revealed that ‘Red xiangzhenzhu’ had the highest quality and ‘Hongjiangci10’ and ‘Ziluolan’ the lowest quality. Cluster analysis using k-means grouped the cultivars into three clusters based on their quality: Cluster 1 grouped those with inferior quality (‘Hongjiangci09’, ‘Hongjiangci10’, ‘Hongjiangci11’, and ‘Hongjiangci07’, etc.), Cluster2 grouped those with average quality (‘Ciputao3#,’ ‘Ziluolan’, and ‘Xiangci4#’), and Cluster3 grouped those with superior quality (‘Red xiangzhenzhu’ and ‘Green xiangzhenzhu’). A combination of principal component analysis and cluster analysis provides a comprehensive and objective evaluation system for determining the quality of grape cultivars. This study is important for the systematic evaluation and utilization of spine grape resources.
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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 (July 15, 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 variance of 32.10% had contribution from days to 50% flowering, panicle length, panicles per square meter and grain yield while principal component 2 accounting 23.50% total variability has contribution from days to 50% flowering and panicles per square meter. The remaining variability of 17.68%, 16.10% and 10.60% was consolidated in PC3, PC4 and PC5. Results from cluster analysis grouped 100 germplasm lines into four clusters with minimum individuals constituted in cluster 1 and maximum individuals were found in cluster 4. The lines in cluster 1 (2.62) showed maximum divergence followed by cluster 3 (2.23). The maximum inter cluster Euclidean distance was observed between clusters 2 and cluster 3 followed by cluster 1 and cluster 2 giving a scope for selection of parents for hybridization programme from these clusters to realize high genetic variation and novel combinations for yield increment.
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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 (March 2, 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 number of companies. The experiential exercise forms an optimal consortium of four companies from six shortlisted competitors
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Singh, Jay, C. L. Maurya, Rishabh Gupta, Sunil Kumar, Shivam Chaturvedi, Ajay Pratap Singh, and Dhruvendra Singh Sachan. "Genetic Divergence Analysis of Wheat (Triticum aestivum L.) Genotypes." Journal of Experimental Agriculture International 46, no. 5 (March 21, 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 grouped into 5 clusters resulted in cluster I and V emerging with highest number of entries as both were constituted by 9 entries followed by Cluster III having 6 genotypes and cluster II and VI having 4 genotypes respectively. The maximum intra cluster distance was estimated in the case of Cluster II (2.630) followed by cluster I (2.618), Cluster III (2.545), cluster IV (2.512), and cluster V (2.148). The highest inter-cluster distance was observed between clusters III and IV (6.062) Followed by clusters III and V (4.632), II and IV (4.489), cluster II and V ( 4.304). The minimum inter cluster was observed between II and III (3.317) followed by cluster IV and V ( 3.337) and cluster I and V (3.404). The cluster mean of 13 different characters for most of the character's highest cluster mean was observed in clusters IV and V and lowest cluster mean observed in cluster III and II.
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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 (September 11, 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 (4.193) followed by cluster I and cluster IV (4.339) and cluster II and cluster III(4.390) indicating that the genotypes of these clusters were genetically close. The intra cluster distance of all the four clusters was more or less low which indicated that the genotypes within the same cluster were closely related. The highest inter genotypic distance (0.9166) was observed between the genotypes G 40 and G 41and lowest (0.0993) between the genotypes G 22 and G 43. Among the characters, heading days, maturity days, plant height (cm), canopy temperature at vegetative stage, canopy temperature at grain filling stage, grain filling rate (g d-1m-2), 1000- grain weight (g), and grains spike-1 contributed most for divergence in the studied genotypes. Cluster I had the highest mean for grain yield (4711.2 kg/ha), grain filling rate (17.5 g d-1m-2), chlorophyll content at anthesis, and plant height (93 cm). Crosses between I & III, I & II, and III & IV have greater chances to generate more heterotic F1s. Considering magnitude of genetic distance, contribution of different traits toward the total divergence, magnitude of cluster means for different traits and performance the genotypes G10, G 11, G12, G35, G40, G48 of cluster I, G7 of cluster II, G41, G5,and G3 of cluster III and G46, G21 of cluster IV may be considered as good parents for future hybridization program to produce high yielding genotypes. DOI: http://dx.doi.org/10.3329/bjar.v39i2.20414 Bangladesh J. Agril. Res. 39(2): 189-196, June 2014
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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 desirable rating for the highest plant height, number of branches per plant, and kernel yield per plant. Cluster X had a desirable rating for the highest germination percentage, earliness flowering, the highest number of kernels per plant, and shelling percentage. Cluster III reported the greatest intra-cluster distance. Cluster IV and Cluster VII were established to possess the greatest inter-cluster distance.
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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 (June 30, 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 by Cluster III and Cluster IV. The results revealed that the parents in these clusters are genetically heterogeneous. It's possible that a hybridization program obtained a significant heterotic response. Clusters I and II found the minimum inter-cluster distances, indicating limited genetic diversity. Cluster III had the maximum seed yield per plant cluster value. Individual performance was highest for the genotypes BINAsoybean-3, BINAsoybean-2, and Shohag for the trait seed yield per plant.
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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 (November 10, 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 grouped into 5 clusters in which cluster I was the largest consistin of 29 genotypes. While cluster IV & V were the smallest with only a single genotypes; each. Maximum intra cluster distance was found in the cluster II, Which comprises only 5 genotypes. The most divergent clusters observed were cluster III & V. The minimum cluster distance was recorded between cluster I & III.
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Staszewska, Jolanta. "CLUSTER DEVELOPMENT – INTERNATIONAL ANALYSIS OF AUTOMOTIVE CLUSTERS." Zeszyty Naukowe Wyższej Szkoły Humanitas Zarządzanie 20, no. 2 (June 30, 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 approach through SWOT / TOWS. Factors stimulating the development of the automotive cluster in Poland and Slovakia in relation to the final results of strategic analysis were indicated.
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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 transitive closure operation with respect to the equivalence that is obtained, such connections between objects as similarity and dissimilarity are changed. Therefore, it is necessary to take into account the problem of adequacy when developing models and algorithms for solving problems of fuzzy cluster analysis. The purpose of the paper is an analyzing the problem of adequacy of the results of fuzzy cluster analysis on the introduction of metrics and pseudometrics on fuzzy sets in the presence of several qualitative and quantitative characteristics of objects. Propose an approach that ensures the adequacy of pseudometrics, that is, provides invariance with respect to permissible transformations of the values of fuzzy features, and also ensures the division of objects into equivalence classes without distorting the distance between them. Results. Axiomatic definitions of a fuzzy cluster and a fuzzy α level cluster are proposed, which are introduced as fuzzy sets of elements similar to certain elements of a given set, if the condition is met: the dissimilarity ratio must be an invariant pseudometric. This condition is ensured by the use of the linguistic correlation coefficient when calculating fuzzy relations of similarity and dissimilarity. Based on the definition of a fuzzy cluster of α level and threshold conorm, the distance between fuzzy clusters of α level is determined. Conclusions. The proposed approach can be the basis for the development of algorithms for solving cluster analysis problems. This provides a meaningful interpretation of the obtained clusters, and the possibility of clarifying the results in further studies of their structure. Keywords: fuzzy set, conorm, metric, pseudometric, fuzzy similarity relation, fuzzy cluster.
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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 had late flowering time. The inbred lines grouped into the cluster III were characterized by higher value of anthesis silking interval (ASI) and those of cluster VI had lower value of ASI. These results showed that the inbred lines having widely divergent clusters can be utilized in hybrid breeding programme.
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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 specialization and name are formed by the cluster core companies, and this is of fundamental importance. Companies that belong to one cluster core cannot access the core of another cluster, but can participate in another cluster at the second or third level of the cluster structure. As the companies in the cluster core produce the same type of products, competition between them will continue. A key feature of the cluster core is the competition between the companies that make it up, i.e. there is both competition and collaboration in the cluster core. As a result authors, proposals were made to improve the efficiency of clusters in Uzbekistan.
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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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Jatothu, Jawahar Lal, A. Anil Kumar, S. B. Choudhary, H. K. Sharma, R. T. Maruthi, C. S. Kar, and J. Mitra. "Genetic diversity analysis in tossa jute (Corchorus olitorius L.) germplasm lines." Journal of Applied and Natural Science 10, no. 1 (March 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 height, basal diameter, green weight and stick weight. The highest inter- cluster distance was 186.80 (between clusters II and X) followed by 161.26 (between clusters IV and X), indicating the wide genetic diversity among these clusters. The highest intra-cluster distance was observed in cluster II (20.34) and the lowest in cluster X (3.17). The average inter-cluster distances were higher than the average intra-cluster distances, which shows the presence of wide genetic diversity among the genotypes of different clusters than those of the same cluster. The first two principal components, whose Eigen values are greater than one, accounted for 74% of the total variation among the five characters. The information obtained from diversity analysis is useful in planning further breeding programme for tossa jute improvement.
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Кыдырбаева, И. "CLUSTER FORMATION POTENTIAL ANALYSIS." Vestnik Bishkek state university af. K. Karasaev 1, no. 59 (April 27, 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 of equipment, components, specialized production and services, research and educational organizations that form economic clusters in the regions. This article also highlights the main directions of regional cluster policy.
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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 (January 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 cluster-location quotient (OCIC-LQ), and recent research on computerization and automation of occupations. The research questions include how knowledge occupation clusters differ in specialization within the select logistics clusters. How can occupation clusters inform the traditional cluster-based economic development policies in the U.S.? How might automation impact the logistics cluster? The findings show that transportation and logistics clusters are unique in knowledge-based occupations with some commonalities found in different locations. Based on occupational and staffing patterns, nearly 71% of occupations or tasks and activities within the transportation and logistics cluster in Memphis is at risk of automation. The research builds a case for place-based cluster development and people-based workforce development for transportation and logistics cluster in the U.S.
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Altomare, Michele, Andrea Chierici, Francesco Virdis, Andrea Spota, Stefano Piero Bernardo Cioffi, Shir Sara Bekhor, Luca Del Prete, 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 (June 24, 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 period, the centralization of major trauma has deeply impaired the liver procurement and allocation between the transplant centers in the metropolitan area of Milan (Niguarda: 22 liver procurement; other transplant centers: 2 organ procurement). Two clusters were identified the in Niguarda’s series: cluster 1 is represented by 17 (27.4%) trauma donors, of which 13 (76.5%) were treated with DCS procedures, and 4 (23.5%) did not; cluster 2 is represented by 45 trauma donors (72.6%), of which 22 (48.8%) underwent DCS procedures. A significant difference was found in the number of DCS procedures performed between clusters (3.18 ± 2.255 vs. 1.11 ± 1.05, p = 0.0001). Comparative analysis did not significantly differ in the number of transplanted livers (cluster1/cluster2 94.1%/95.6% p = 0.84) and the 30-day graft dysfunction rate (cluster1/cluster2 0.0%/4.8% p = 0.34). Conclusions: The high level of care guaranteed by first-level trauma centers could reduce the loss of organs suitable for donation, maintaining the good outcomes of transplanted ones, even in case of multiple organ injuries. The pandemic period underlined that the centralization of major trauma impairs the liver allocation between transplant centers.
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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 (December 1, 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 0.19 to 0.59) and grouped the genotypes into 6 zymotypes. Zymotype P4 included maximum (13) number of genotypes. Number of clusters in peroxidase and multivariate analysis were almost same but distribution of genotypes varied. 73% of total genotypes showed similar pattern of grouping suggesting that the two methods are complementary to each other and should be carried out simultaneously to determine genetic diversity more effectively. Considering variability and diversity analysis of the genotypes, CUCH-4 from Cluster-II (& Zymotype-P2) and CUCH-31, CUCH-34 and CUCH-35 from Cluster-VII (& Zymotype-P4) were identified as promising genotypes which can be used in further crop improvement programme.
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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 (June 7, 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 clusters selected through a multivariate approach. As a result, the physical health clusters could be subdivided into four clusters: balance (1), muscular strength (2), low agility (3), and low physical fitness (3) cluster. Cluster 1 showed a high ratio of slim and slightly slim children, while cluster 2 had a high proportion of children that were obese, tall, or heavy, and had the highest BMR. We consider such results as important primary data for constituting physical fitness management programs customized to each cluster. It seems that it is necessary to have a multidirectional approach toward physical fitness evaluation and analysis methodologies that involve various physical fitness factors of children.
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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 (April 15, 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, such as allergenicity (whether the product contains an allergic component or not) or carbohydrate content (important for diabetics). The content of protein, potassium and phosphates should be taken into account when developing diets for those suffering from kidney diseases. The presence of specific amino acids - for metabolic diseases, etc. In this way, food composition data and product clustering across different categories allow nutritionists to create interchangeable lists of meals with portion sizes, or lists of permitted and prohibited food products in terms of various diseases. The authors give the clustering of the database fragment of chemical composition of food products on the example of cottage cheese products and confectionary by one of the signs – the content of carbohydrates – in the R software environment by k-means. Food clusters based on carbohydrate content are very important in shaping the diet for diabetics. A visual gradation of products into clusters is demonstrated in the form of a dendrogram showing the degree of proximity of individual clusters. The resulting dendrogram contains 5 clusters. Cluster 4 includes the largest number of products (170 items) with an average carbohydrate content of 1.8 g with a variation range from 0 to 7.1 g. Food products and dishes that fall into this cluster are the least dangerous for people with diabetes. Cluster 5 includes only 8 products with a distribution of carbohydrates within the cluster from 62.60 to 80.40 g. This category of food should be excluded when preparing a diet for people with diabetes.
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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 (December 17, 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 maximum genotypes (5) followed by cluster I having 4 genotypes and cluster II having 3 genotypes. Cluster III had the minimum genotypes (2). The inter-cluster distances were greater than intra-cluster distances in all cases, suggesting wider genetic diversity among the genotypes of different groups. The highest intracluster distance was observed in cluster III and the lowest in cluster II. The maximum inter-cluster distance was estimated between clusters I and IV (11.3212), moderate distance between clusters II and IV (9.961) and clusters III and IV (7.568), and that of the lowest between clusters I and III. Cluster III recorded the highest mean values for fruit length, plant height at last harvest, number of fruits/plant, weight of fruits/plant and fruit yield, while cluster IV exhibited the maximum mean values for pulp thickness, plant height at 1st harvest and the second highest mean values for fruit length, fruit breadth and TSS. Therefore, more emphasis should be given on cluster III for selecting genotypes as parents for crossing with the genotypes of cluster IV which may produce new recombinants with desired traits.Bangladesh J. Agril. Res. 41(4): 647-656, December 2016
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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 (April 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, SS-A association; cluster 2 had no definite autoantibody association; and cluster 3 was associated with nucleosome, histone, dsDNA, and ribosomal P protein. Patients in cluster 3 had a highly active disease while those in cluster 1 had significant cytopenia. Mean age and mean right ventricular systolic pressure (RVSP) were both high in cluster 2, indicating later-onset PAH in this group. This was the first autoantibody-based cluster analysis study in SLE-PAH patients in India which confirmed that autoantibodies did exist as clusters and the presence of definite autoantibodies can predict future development of pulmonary hypertension in these patients.
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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 cluster result into a weighted bipartite network and then the optimal cluster number can be detected by the improved bipartite modularity. The experimental results on artificial and real data sets show the validity of the proposed method.
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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 (September 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 generates clusters that can overlap. This paper explains how to prepare card sort data for statistical analysis, describes the results of factor analysis and how to interpret them, and discusses when hierarchical cluster analysis and factor analysis are appropriate.
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Kohberger, Robert C., and Brian S. Everitt. "Cluster Analysis." Technometrics 36, no. 2 (May 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 (March 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 (October 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 (April 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 (October 19, 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 (December 16, 2006): 972–83. http://dx.doi.org/10.1111/j.1749-6632.1965.tb11711.x.
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Bratchell, N. "Cluster analysis." Chemometrics and Intelligent Laboratory Systems 6, no. 2 (July 1989): 105–25. http://dx.doi.org/10.1016/0169-7439(87)80054-0.
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Sopp, P. "Cluster analysis." Veterinary Immunology and Immunopathology 52, no. 4 (August 1996): 237–44. http://dx.doi.org/10.1016/0165-2427(96)05567-5.
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Everitt, Brian. "Cluster analysis." Personality and Individual Differences 8, no. 6 (January 1987): 985. http://dx.doi.org/10.1016/0191-8869(87)90161-9.
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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 (June 4, 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 distance and Cluster I had the lowest intra cluster distance. Inter cluster distance was maximum (11.697) between clusters II and III. The results revealed that genotypes chosen for hybridization from clusters with highest distances would give high heterotic F1 and broad spectrum of variability in segregating generations. The characters- number of primary branches/plant, number of secondary branches/plant and days to 50% flowering contributed maximum towards divergence among Brassica genotypes. Considering cluster distance, inter genotypic distance and other agronomic performance G2 and G14 from cluster I; G18 from cluster II; G1, G9 and G12 from cluster III and G16 and G17 from cluster IV may be considered as better parents for future uses in hybridization program. Bangladesh J. Agril. Res. 38(1): 11-18, March 2013 DOI: http://dx.doi.org/10.3329/bjar.v38i1.15185
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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 (April 2, 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 inter-cluster distance was observed between cluster II and cluster III (3.034) followed by cluster 111 and cluster IV (3.834) and cluster II and cluster V (4.945) indicating that the genotypes of these clusters were genetically close. The intra cluster distance in the entire five clusters was more or less low which indicated that the genotypes within the same cluster were closely related. Among the characters panicle weight contributed most for divergence in the studied parental lines. Difference in cluster means existed for almost all the characters studied. Highest mean value for number of effective tillers (7.8), days to 50% flowering (95.5), panicles/m2 (192.6), panicle weight (2.9), spikelet fertility (84.8), number of grains/panicle (177.8), days to maturity (123.6), and grain yield/plot (1065.5) were observed in cluster I indicated the parental lines fallen in this cluster having the genetic potentiality to contribute better for yield maximization of hybrid rice. DOI: http://dx.doi.org/10.3329/bjar.v37i4.14386 Bangladesh J. Agril. Res. 37(4): 617-624, December 2012
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Kraska, Ewa. "Analysis of the Functioning of Clusters in Poland." Journal of International Business Research and Marketing 7, no. 1 (November 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 economic activity generates positive effects for business and the region. Global researchers suggest that clusters help to increase the innovation and competitiveness of the country in which they are located. Since the 90s clusters have become an increasingly important element of economic development and innovation strategy of the European Union and its Member States. In years 2007–2013, clusters are expected to one of the objectives of support for EU regional policy. EU funds destined for cluster initiatives will help to take concrete actions by entrepreneurs interested in the cluster activity. Poland has recently joined the countries interested in popularizing the idea of clusters. Some specialized cluster studies have been carried out in Poland identifying clusters. This article gives an overview on policy support, formation and the functioning of clusters in Poland.
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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 (February 8, 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 and Cluster II followed by cluster III and Cluster II, which indicates that genotypes belonging to these distant clusters could be used in hybridization programme for getting a wide spectrum of variation among the segregates. The minimum intercluster distance was found between the Cluster IV and Cluster I followed by that of Cluster V and Cluster IV. The maximum intra-cluster distance was recorded in Cluster II, consisted of three genotypes of diverse origin followed by Cluster V consisting of five genotypes which indicated that the genotypes of these clusters might have considerable diversity among themselves. While the minimum distance was computed in Cluster I composed of five genotypes which indicated that these genotypes were genetically very close to each other. Considering the eigenvalues of all principal component analysis the PC1, PC2, PC3, PC4, and PC5 with values contributed 30.78%, 20.11%, 17.75%, 10.93%, and 7.63%, respectively, of the total variation. The results revealed from the present study that the first principal component had high positive component loading from grains/spike and high negative loading from grain yield. Considering the clusters mean value, the genotype of Cluster II and VI are most divergent and maximum heterosis and wide variability in genetic architecture may be expected from the crosses between the genotypes belonged to these clusters. More specifically the cluster II could be selected for dwarf in nature, early heading and maturity and bold grain size. The genotypes from the cluster IV could be selected for maximum spikes/m2 and maximum grain yield. The positive value of both vectors for days to heading and spikes/m2 indicated that these traits had the highest contribution towards divergence among the 30 drought tolerant wheat genotypes. DOI: http://dx.doi.org/10.3329/bjar.v39i3.21982 Bangladesh J. Agril. Res. 39(3): 385-396, September 2014
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Hüseynov, N. "APPLICATION OF CLUSTER ANALYSIS OF BANK CUSTOMERS." Intellect. Innovations. Investments, no. 3 (2023): 72–82. http://dx.doi.org/10.25198/2077-7175-2023-3-72.
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Since companies can have thousands and more customers, effective management of this customer base is one of the most important conditions for business success. In order to know the customers, it is possible to categorize them by dividing them into small groups according to their different similarities, and then specify the type of services to be offered to them. Customer segmentation has the potential to make a difference in different businesses. The fact that the variety of products and services offered in the banking sector is increasing day by day and the transition to the digital environment is faster in this sector shows that the correct segmentation of the customers of the banks will save them more profit and time in this competitive market. In this study the main intention is to divide customers into small manageable groups using clustering algorithms and to find the relative importance of these groups using multi-criteria decision-making technique. In this regard, the customer segmentation approach was implemented in one of the banks operating in Azerbaijan. Currently, the bank is one of the financial institutions with the largest service network in Azerbaijan. The bank in question provides services to more than 5 million individuals and more than 22 thousand legal entities. In addition to these, it closely participates in a number of social software applications developed by the state and applies a several of the programs for the improvement of the real sector.
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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 (July 25, 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 radar data in two air traffic control sectors were collected to assess the cluster division results. Through statistics, the threshold intervals with 95% of best performance are 40–60 km and 20–50 km for the two sectors, respectively. The value 40 km was selected to further statistically characterize the aircraft clusters. Compared with K-means clustering, the proposed method does not require the predefined number of clusters and has high stability, which confirms its feasibility into cluster division in dynamic air traffic situation. The structural characteristics of aircraft clusters, including the average intra-cluster horizontal distance, number of clusters, and size and life cycle of clusters, were statistically analyzed. Comparison of cluster structures with the commonly used dynamic density index shows that in air traffic situation with relatively large number big size of clusters, the aircraft trajectory changes more frequently. Structural characterization of aircraft clusters is able to portray the nonuniformity of traffic density distribution, and contributes to comprehensive description of air traffic situation, thus providing a new prospect for analysis of air traffic complexity. Moreover, aircraft cluster division contributes to auto-identification of hot-spots on radar screen, and efficiently eliminates the workload imposed on controllers during judgment of these congestion hot-spots, thereby improving the air traffic operation efficiency.
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Zahara, Yulia, Nurul Afni Sinaga, Rifaatul Mahmuzah, and Hidayatsyah Hidayatsyah. "Analysis of Senior High Schools Quality in Banda Aceh Using Cluster and Correspondence Analysis." PARADIKMA: JURNAL PENDIDIKAN MATEMATIKA 16, no. 1 (January 31, 2023): 1–14. http://dx.doi.org/10.24114/paradikma.v16i1.41846.
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The purpose of this research is to analyze the relationship between teachers and school conditions on school test scores. Correspondence and cluster analysis are the analytical methods used. For school conditions to be classified into 3 clusters, 4 clusters, and 5 clusters, cluster analysis is used. The variable is tested using the G test to determine the affect on school test scores. This study resulted in a variable that has an impact on school test scores, school conditions with a p-value < α, so a correspondence analysis plot was formed to show the correlation between school conditions and school test scores. Good school conditions will result in good school test scores. Meanwhile, a reasonably good school conditions will result in fairly good school test scores. The correlation of school test scores with teachers shows that good school test scores are resulted by teachers with very good quality. Fairly good school test scores are resulted by good and fairly good teachers. Meanwhile, poor school test scores are resulted by teachers with poor quality.
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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 (February 28, 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 selected at random, marked, and observations were recorded for twelve quantitative attributes. Analysis of variance showed that there was significant variation among all characters examined. The twenty-five genotypes of mungbean have been split into seven distinct clusters. With seven genotypes apiece, Cluster I and Cluster IV were determined to be the largest. The intra cluster distance was maximum for Cluster IV. The maximum inter cluster distance between cluster V and cluster II suggests that the genotypes in these clusters doesn't correlate with one another and the minimal inter-cluster distance between cluster V and cluster IV demonstrates a high degree of connection between the genotypes in these clusters. Based on high inter cluster distances, hybridization programme could be taken up between the varieties of cluster II (Pusa Vaishali, IPM-02-19, IPM 02-19, OMG-1045, VBG-04-008) and cluster V (Pusa-0871, Pusa-0891, SMM-15-72, PDM-262).Hence, these nine genotypes are recognized as promising progenitors and can be employed in further breeding programme. Plant height, number of branches/plants, number of pods/plant, number of pods/clusters, pod length, biological yield, harvest index and seed yield per plant are vital for genetic diversity and were recognized as significant contributors to genetic divergence.
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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 with relatively larger leaves, thicker stems and larger panicles. Cluster VI displayed highest panicle number per plant, longest grain length and lowest mean days for time maturity. Genotypes in these clusters could be explored further for their potential in enhancing yield and quality characteristics.
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Reddy, Annem Sethuvardhan, Gayathri G., and Amaranatha Reddy. "Cluster Analysis in Fodder Oats (Avena sativa L.)." International Journal of Plant & Soil Science 36, no. 7 (June 21, 2024): 576–82. http://dx.doi.org/10.9734/ijpss/2024/v36i74768.
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Oats is an important crop used globally for food and fodder, holding significant economic value. In the context of livestock farming, fodder oats (Avena sativa L.) is an important crop during winter season. A comprehensive analysis of thirty oat genotypes was conducted at The Regional Agricultural Research Station at Ambalavayal in Wayanadan Eastern Plateaux of Kerala to identify superior genotypes that could increase forage production and improve nutritional quality during the Rabi season of 2022-23. A cluster analysis using Mahalanobis D2 statistics was performed employing the Tocher method within the Indostat software, involving eleven morphological and six nutritional traits. The thirty genotypes were categorized into seven clusters based on their D2 values using the Euclidean method. Cluster I consisted of 14 genotypes (OL-1942, OL-1944, OL-1980, OL-15, OL-212, OL-11, OL-1952, OL-1874-1, OL-1975-2, OL-1976-1, OL-12, OL-1967, AVT-1, OL-13), followed by Cluster II with 8 genotypes (OL-10, OL-2000, OL-1977, OL-1964, OL-1988, OL-1896, OL-1802, OL-1974). Cluster IV comprised 3 genotypes (OL-1937, OL-1963, OL-125), Cluster III included two genotypes (OL-9, JHO-822), and Cluster V(OL-1931-1), VI(OL-1969), and VII(OL-1949) each had one genotype. The inter-cluster D2 values were found to be higher than the intra-cluster D2 values implying that there is a substantial amount of diversity among the genotypes under study with respect to the considered characters. The highest intra-cluster distance was observed in Cluster IV (42.81), followed by Cluster II, Cluster I, and Cluster III. The maximum inter-cluster D2 values were observed between Clusters IV and VII (102.31), and the minimum was observed between Clusters II and V (45.44). Based on the cluster mean, Cluster III was observed to be a significant contributor of days to first and 50% flowering, days to maturity, crude fibre content, total phenolic and antioxidant content. Cluster IV was a potential contributor to green fodder yield, dry matter yield, leaf and stem dry weight, plant height, and phytate content. Cluster V was associated with the number of tillers and crude protein content. Cluster VI was related to the number of leaves and condensed tannin content. Cluster VII was pertaining to the leaf-stem ratio.