Relevant bibliographies by topics / Cluster analysis

Academic literature on the topic 'Cluster analysis'

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Published: 4 June 2021
Last updated: 29 July 2024

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Journal articles on the topic "Cluster analysis"

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 (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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2

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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Dissertations / Theses on the topic "Cluster analysis"

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Kozovska, Kornelia <1981&gt. "Business Clusters in Eastern Europe: Policy Analysis and Cluster Performance." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2009. http://amsdottorato.unibo.it/1611/2/Tesi_Kornelia_Kozovska.pdf.

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Clusters have increasingly become an essential part of policy discourses at all levels, EU, national, regional, dealing with regional development, competitiveness, innovation, entrepreneurship, SMEs. These impressive efforts in promoting the concept of clusters on the policy-making arena have been accompanied by much less academic and scientific research work investigating the actual economic performance of firms in clusters, the design and execution of cluster policies and going beyond singular case studies to a more methodologically integrated and comparative approach to the study of clusters and their real-world impact. The theoretical background is far from being consolidated and there is a variety of methodologies and approaches for studying and interpreting this phenomenon while at the same time little comparability among studies on actual cluster performances. The conceptual framework of clustering suggests that they affect performance but theory makes little prediction as to the ultimate distribution of the value being created by clusters. This thesis takes the case of Eastern European countries for two reasons. One is that clusters, as coopetitive environments, are a new phenomenon as the previous centrally-based system did not allow for such types of firm organizations. The other is that, as new EU member states, they have been subject to the increased popularization of the cluster policy approach by the European Commission, especially in the framework of the National Reform Programmes related to the Lisbon objectives. The originality of the work lays in the fact that starting from an overview of theoretical contributions on clustering, it offers a comparative empirical study of clusters in transition countries. There have been very few examples in the literature that attempt to examine cluster performance in a comparative cross-country perspective. It adds to this an analysis of cluster policies and their implementation or lack of such as a way to analyse the way the cluster concept has been introduced to transition economies. Our findings show that the implementation of cluster policies does vary across countries with some countries which have embraced it more than others. The specific modes of implementation, however, are very similar, based mostly on soft measures such as funding for cluster initiatives, usually directed towards the creation of cluster management structures or cluster facilitators. They are essentially founded on a common assumption that the added values of clusters is in the creation of linkages among firms, human capital, skills and knowledge at the local level, most often perceived as the regional level. Often times geographical proximity is not a necessary element in the application process and cluster application are very similar to network membership. Cluster mapping is rarely a factor in the selection of cluster initiatives for funding and the relative question about critical mass and expected outcomes is not considered. In fact, monitoring and evaluation are not elements of the cluster policy cycle which have received a lot of attention. Bulgaria and the Czech Republic are the countries which have implemented cluster policies most decisively, Hungary and Poland have made significant efforts, while Slovakia and Romania have only sporadically and not systematically used cluster initiatives. When examining whether, in fact, firms located within regional clusters perform better and are more efficient than similar firms outside clusters, we do find positive results across countries and across sectors. The only country with negative impact from being located in a cluster is the Czech Republic.
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2

Kozovska, Kornelia <1981&gt. "Business Clusters in Eastern Europe: Policy Analysis and Cluster Performance." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2009. http://amsdottorato.unibo.it/1611/.

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Clusters have increasingly become an essential part of policy discourses at all levels, EU, national, regional, dealing with regional development, competitiveness, innovation, entrepreneurship, SMEs. These impressive efforts in promoting the concept of clusters on the policy-making arena have been accompanied by much less academic and scientific research work investigating the actual economic performance of firms in clusters, the design and execution of cluster policies and going beyond singular case studies to a more methodologically integrated and comparative approach to the study of clusters and their real-world impact. The theoretical background is far from being consolidated and there is a variety of methodologies and approaches for studying and interpreting this phenomenon while at the same time little comparability among studies on actual cluster performances. The conceptual framework of clustering suggests that they affect performance but theory makes little prediction as to the ultimate distribution of the value being created by clusters. This thesis takes the case of Eastern European countries for two reasons. One is that clusters, as coopetitive environments, are a new phenomenon as the previous centrally-based system did not allow for such types of firm organizations. The other is that, as new EU member states, they have been subject to the increased popularization of the cluster policy approach by the European Commission, especially in the framework of the National Reform Programmes related to the Lisbon objectives. The originality of the work lays in the fact that starting from an overview of theoretical contributions on clustering, it offers a comparative empirical study of clusters in transition countries. There have been very few examples in the literature that attempt to examine cluster performance in a comparative cross-country perspective. It adds to this an analysis of cluster policies and their implementation or lack of such as a way to analyse the way the cluster concept has been introduced to transition economies. Our findings show that the implementation of cluster policies does vary across countries with some countries which have embraced it more than others. The specific modes of implementation, however, are very similar, based mostly on soft measures such as funding for cluster initiatives, usually directed towards the creation of cluster management structures or cluster facilitators. They are essentially founded on a common assumption that the added values of clusters is in the creation of linkages among firms, human capital, skills and knowledge at the local level, most often perceived as the regional level. Often times geographical proximity is not a necessary element in the application process and cluster application are very similar to network membership. Cluster mapping is rarely a factor in the selection of cluster initiatives for funding and the relative question about critical mass and expected outcomes is not considered. In fact, monitoring and evaluation are not elements of the cluster policy cycle which have received a lot of attention. Bulgaria and the Czech Republic are the countries which have implemented cluster policies most decisively, Hungary and Poland have made significant efforts, while Slovakia and Romania have only sporadically and not systematically used cluster initiatives. When examining whether, in fact, firms located within regional clusters perform better and are more efficient than similar firms outside clusters, we do find positive results across countries and across sectors. The only country with negative impact from being located in a cluster is the Czech Republic.
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3

Pavlou, M. "Analysis of clustered data when the cluster size is informative." Thesis, University College London (University of London), 2012. http://discovery.ucl.ac.uk/1357842/.

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Clustered data arise in many scenarios. We may wish to fit a marginal regression model relating outcome measurements to covariates for cluster members. Often the cluster size, the number of members, varies. Informative cluster size (ICS) has been defined to arise when the outcome depends on the cluster size conditional on covariates. If the clusters are considered complete then the population of all cluster members and the population of typical cluster members have been proposed as suitable targets for inference, which will differ between these populations under ICS. However if the variation in cluster size arises from missing data then the clusters are considered incomplete and we seek inference for the population of all members of all complete clusters. We define informative covariate structure to arise when for a particular member the outcome is related to the covariates for other members in the cluster, conditional on the covariates for that member and the cluster size. In this case the proposed populations for inference may be inappropriate and, just as under ICS, standard estimation methods are unsuitable. We propose two further populations and weighted independence estimating equations (WIEE) for estimation. An adaptation of GEE was proposed to provide inference for the population of typical cluster members and increase efficiency, relative to WIEE, by incorporating the intra-cluster correlation. We propose an alternative adaptation which can provide superior efficiency. For each adaptation we explain how bias can arise. This bias was not clearly described when the first adaptation was originally proposed. Several authors have vaguely related ICS to the violation of the ‘missing completely at random’ assumption. We investigate which missing data mechanisms can cause ICS, which might lead to similar inference for the populations of typical cluster members and all members of all complete clusters, and we discuss implications for estimation.
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4

Ngai, Wang-kay, and 倪宏基. "Cluster analysis on uncertain data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B4218261X.

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Dufour, Alyssa Beth. "Cluster analysis of longitudinal trajectories." Thesis, Boston University, 2013. https://hdl.handle.net/2144/12751.

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Thesis (Ph.D.)--Boston University
Cluster analysis is widely used in many disciplines including biology, psychology, archaeology, geography, and marketing. Methods have been developed to extend cluster analysis to longitudinal data, clustering subject trajectories rather than single time points. Here, I examine 2 methods of longitudinal cluster analysis: k-means and model-based (implemented using FlexMix in R) cluster analysis. I compare these two methods based on the Correct Classification Rate, the ability of the method to correctly classify subject trajectories into groups, using a simulation study. Both methods are found to perform well under most circumstances, but in 64% of the scenarios examined, the model-based method out-performs the k-means approach. Next, I examine three criteria that have been used to determine how many groups exist in the data: the Akaike's Information Criteria (AIC), the Davies-Bouldin Index (DB), and the Calinski-Harabasz pseudo F-statistic (CH). The latter two were developed specifically for choosing the number of groups in a cluster analysis with a single observation per person, while the AIC was developed as a general model fit statistic. Few studies have used these criteria in the context of longitudinal data and no study has compared their efficacy. We found that the DB and CH fail to correctly identify the number of groups in the majority cases, while the AIC was better able to determine the correct number. Finally, as no study has examined the addition of a covariate to cluster analysis, we compare results of a cluster analysis when a covariate was taken into account to when it is ignored. When a covariate is both time-dependent and associated with the outcome, regardless of the magnitude of the association, it is important to take this variable into account in the analysis. If the covariate is associated only with the outcome and not time-dependent, depending on the magnitude of the association, it may be necessary to account for the covariate. In summary, we present methods for clustering trajectories, evaluate methods for determining the number of groups and determine the importance of adjusting for covariates in the cluster analysis of longitudinal data.
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Ngai, Wang-kay. "Cluster analysis on uncertain data." Click to view the E-thesis via HKUTO, 2008. http://sunzi.lib.hku.hk/hkuto/record/B4218261X.

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Takahashi, Atsushi. "Hierarchical Cluster Analysis of Dense GNSS Data and Interpretation of Cluster Characteristics." Kyoto University, 2019. http://hdl.handle.net/2433/244510.

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8

Popescu, Bogdan. "MASSCLEAN - MASSive CLuster Evolution and ANalysis Package - A New Tool for Stellar Clusters." University of Cincinnati / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1276526207.

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9

Cuciti, Virginia <1989&gt. "Cluster-scale radio emission: analysis of a mass-selected sample of galaxy clusters." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amsdottorato.unibo.it/8540/1/Tesi_PhD.pdf.

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Radio halos are Mpc scale diffuse sources located at the center of a fraction of galaxy clusters. In the current theoretical picture, they form via the re-acceleration of electrons in the ICM by means of turbulence, injected during cluster mergers. This scenario allows basic predictions on the formation history of radio halos that can only be tested by analysing large samples of galaxy clusters with adequate radio and X-ray data. The main goal of this Thesis is to study the first complete large sample of mass-selected galaxy clusters to obtain solid statistical constraints on the connection between radio halos and the dynamics and mass of the host clusters. We used the Planck SZ catalogue to select a sample of 75 massive galaxy clusters (M500>6x10^{14}Msun) at redshift z=0.08-0.33 and we collected information on the presence or absence of diffuse emission from the literature and from the large observational (GMRT and JVLA) campaign carried out during this PhD project. We analysed X-ray Chandra and XMM-Newton data to investigate the dynamical properties of clusters. We updated the radio power-mass scaling relation for radio halos and we found clear evidence for a bimodal behaviour of clusters in both the radio power-mass plane and, for the first time, in the radio emissivity-mass diagram, with radio halos and non-radio halo clusters following two distinct distributions. Similarly to previous studies, we found that this bimodality is clearly connected to the cluster dynamics. For the very first time, we found an increase of the radio halo fraction with the cluster mass, which is remarkably in agreement with theoretical models. In addition to the statistics of radio halos, the amount of data available in this Thesis led to the discovery of new radio relics, mini halos and head tail radio galaxies in our clusters.
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10

Bozkirlioglu, Ali. "Cluster Potential In Industrial Sectors Of Samsun: Kutlukent Furniture Cluster Study." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/3/12605603/index.pdf.

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The present study investigated whether cluster potentials could be identified in the geographical area within the boundaries of Samsun province, and if identified, how such a potential could be promoted through corresponding support measures. Development of policy recommendations for promotion of identified cluster potential was the principal goal of the study. The course of the study was characterized by a cluster-based policy-making process in the policy environment, i.e. Samsun province. The process includes a descriptive part, i.e. cluster analysis, and a prescriptive part, i.e. determining policy goals and designing policy instruments. In the literature review, a guide to the field study was developed by review of various approaches to cluster concept
common features of clusters and the competitive advantages these give rise to
various practices in cluster-based policy development, and various cluster analysis methods. The field study starts with the initial identification of need for policy intervention, at which stage the rationale for pursuing a cluster-based policy in the specific conditions of Samsun and Turkey was discussed. The &ldquo
clusters as sectors&rdquo
approach was utilized in the identification of region&rsquo
s (potential) clusters and selection of the cluster as the subject of analysis and policy development. The analysis of industrial sectors in Samsun&rsquo
s economy was followed by selection of the target sector via employing various criteria assessing the importance of these sectors in terms of value added to the regional economy, and the clustering potential. Accordingly, furniture sector was selected, and the agglomeration of furniture sector enterprises in Kutlukent locality was identified as the potential cluster to be the subject of analysis and policy development. Following the identification of the potential cluster, the descriptive part was completed by second-stage micro-level analysis of the identified potential cluster, by which detailed information about the potential cluster was presented. At that phase, cluster potential of the structure was assessed by examining the elements in cluster value and production chain
public and private business support infrastructure
the flow of materials and goods in the chain
untraded relationships between the elements
characteristics of enterprises and workforce
and innovation performance. This comprehensive in-depth analysis of the cluster provided the required information to identify the specific needs of the cluster for cluster-based policy intervention. In the last part of the thesis, i.e. prescriptive part, cluster-oriented policy recommendations were developed including the determination of policy goal and the design/selection of policy instruments. The necessary information was collected by two-stage expert interviews, and by overall scan of the enterprises involved in the cluster via enterprise survey, which was realized in interviews with all of the enterprises. Six experts and 283 enterprises participated in the study. The results of the analysis showed that, while Kutlukent furniture cluster had some features, which are common in effective cluster models, the cluster lacks some critical features, which are crucial for effective functioning of a successful cluster. Hence, Kutlukent furniture cluster was defined as a &ldquo
potential&rdquo
cluster, which should be promoted by utilizing the existing potentials and strengths, and by addressing the weaknesses and obstacles identified in the analysis of the cluster, via appropriate cluster-oriented policy measures, which were proposed in the prescriptive part of the policy-making process. By these measures, the elements of Kutlukent potential cluster would be able to realize competitive advantages associated with clustering as in successful cluster models.
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Books on the topic "Cluster analysis"

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Byrne, David, and Emma Uprichard. Cluster Analysis. 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications Ltd, 2012. http://dx.doi.org/10.4135/9781446261033.

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Everitt, Brian S., Sabine Landau, Morven Leese, and Daniel Stahl. Cluster Analysis. Chichester, UK: John Wiley & Sons, Ltd, 2011. http://dx.doi.org/10.1002/9780470977811.

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3

Social Science Research Council (Great Britain), ed. Cluster analysis. 2nd ed. Aldershot: Published on behalf of the Social Science Research Council by Gower, 1986.

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Sabine, Landau, and Leese Morven, eds. Cluster analysis. 4th ed. London: Arnold, 2001.

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library, Wiley online, ed. Cluster Analysis. 5th ed. Hoboken: Wiley, 2011.

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Everitt, Brian. Cluster analysis. 3rd ed. London: E. Arnold, 1993.

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Murtagh, Fionn. Handbook of cluster analysis. Edited by Meilă Marina 1962 editor. Boca Raton: Taylor & Francis, 2016.

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Andrew, Lawson, and Denison David G. T, eds. Spatial cluster modelling. Boca Raton, FL: Chapman & Hall/CRC, 2002.

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Scitovski, Rudolf, Kristian Sabo, Francisco Martínez-Álvarez, and Šime Ungar. Cluster Analysis and Applications. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74552-3.

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D, Hovee Eric, Oregon Forest Resources Institute, and E.D. Hovee & Company., eds. Oregon forest cluster analysis. Vancouver, Wash: E.D. Hovee & Company, 2005.

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Book chapters on the topic "Cluster analysis"

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Tay, Dennis. "Cluster analysis." In Data Analytics for Discourse Analysis with Python, 66–104. New York: Routledge, 2024. http://dx.doi.org/10.4324/9781003360292-3.

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Backhaus, Klaus, Bernd Erichson, Sonja Gensler, Rolf Weiber, and Thomas Weiber. "Cluster Analysis." In Multivariate Analysis, 451–530. Wiesbaden: Springer Fachmedien Wiesbaden, 2021. http://dx.doi.org/10.1007/978-3-658-32589-3_8.

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Backhaus, Klaus, Bernd Erichson, Sonja Gensler, Rolf Weiber, and Thomas Weiber. "Cluster Analysis." In Multivariate Analysis, 453–532. Wiesbaden: Springer Fachmedien Wiesbaden, 2023. http://dx.doi.org/10.1007/978-3-658-40411-6_8.

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Sarstedt, Marko, and Erik Mooi. "Cluster Analysis." In Springer Texts in Business and Economics, 273–324. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-53965-7_9.

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Gatignon, Hubert. "Cluster Analysis." In Statistical Analysis of Management Data, 453–85. Boston, MA: Springer US, 2013. http://dx.doi.org/10.1007/978-1-4614-8594-0_12.

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Cleff, Thomas. "Cluster Analysis." In Applied Statistics and Multivariate Data Analysis for Business and Economics, 407–31. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17767-6_12.

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Baragona, Roberto, Francesco Battaglia, and Irene Poli. "Cluster Analysis." In Evolutionary Statistical Procedures, 199–260. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16218-3_7.

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Filzmoser, Peter, Karel Hron, and Matthias Templ. "Cluster Analysis." In Springer Series in Statistics, 107–30. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96422-5_6.

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Olson, David L. "Cluster Analysis." In Computational Risk Management, 71–95. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-3340-7_6.

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Härdle, Wolfgang Karl, and Zdeněk Hlávka. "Cluster Analysis." In Multivariate Statistics, 225–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-36005-3_13.

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Conference papers on the topic "Cluster analysis"

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Martin, M. Z., S. R. Desai, C. S. Feigerle, and J. C. Miller. "Laser-Induced Chemistry within Clusters." In Laser Applications to Chemical and Environmental Analysis. Washington, D.C.: Optica Publishing Group, 1996. http://dx.doi.org/10.1364/lacea.1996.lfa.6.

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Clusters comprise an interesting environment for the study of chemical reactions. Binary cluster complexes can be produced which combine a variety of potential reactant partners in various proportions in an intimate, yet controlled arrangement. Molecules or ions within such a cluster (i.e. solvated) have properties which are intermediate between those of isolated species and of species in solution or in bulk. Furthermore as the cluster size increases their properties may smoothly change until the condensed-phase regime is reached. But, since clusters are generated and interrogated in the gas phase, powerful techniques such as mass spectrometry may be used for characterization. Such clusters may then represent model environments which simulate real world situations such as aerosols or cells. Similarly, the chemistry of such clusters may also differ in significant ways from that of the isolated species.
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Gudipaty, Tarun, Luthur S. L. Cheung, Linan Jiang, and Yitshak Zohar. "Cluster Formation and Growth in Flow of Dilute Particle Suspension in Microchannels." In ASME 2008 9th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2008. http://dx.doi.org/10.1115/esda2008-59602.

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Microchannels are susceptible to blockage by solid particles. The lifetime of microfluidic devices depends on their ability to maintain flow without interruption, while certain applications require microdevices for transport of liquids containing particles. In this work, the phenomenon of interest is the formation and growth of clusters in the flow of a dilute suspension of hard spheres. Based on the present experiments, aggregation of clusters was observed for particle-laden flow in a microchannel with particle void fraction as low as 0.001 and particle diameter to channel height ratio as low as 0.1. The incipience and growth of a single cluster is discussed, and the spatial distribution and time evolution of clusters along the microchannel is presented. Although the cluster size seems to be independent of location, higher number of clusters is found at the inlet/outlet regions than in the microchannel center. Similar to individual cluster, the total cluster area in the microchannel grows almost linearly in time. The effects of flow rate, particle size and concentration are also reported.
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Steadman, J., E. W. Fournier, and J. A. Syage. "Detection and Differentiation of Neutral and Ionic Reaction Mechanisms in Molecular Clusters." In Laser Applications to Chemical Analysis. Washington, D.C.: Optica Publishing Group, 1990. http://dx.doi.org/10.1364/laca.1990.pd4.

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A longstanding goal in the chemical analysis of reaction mechanisms is understanding the role of the solvent. We have been involved in work that addresses this issue on a single molecule basis by studying reactions in molecular clusters.1–3 In this report we describe a means for detecting and measuring rapid intermolecular cluster chemistry using mass-selective picosecond resonance-enhanced multiphoton ionization (REMPI). Molecular beam mass spectrometry offers a powerful means for identifying a variety of product species and distinguishing precursor cluster size. However, such investigations demand an independent means for differentiating neutral cluster reactions from ionic reactions. Our approach is to obtain direct measurements of the ion dissociation mechanisms by electron-impact (El) ionization and by mass-selective ion photodissociation.
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Popchev, I., and V. Peneva. "CLUSTER-a package for cluster analysis." In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 1988. http://dx.doi.org/10.1109/iembs.1988.95330.

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Das, Sunanda, Shreya Chaudhuri, and Asit K. Das. "Cluster analysis for overlapping clusters using genetic algorithm." In 2016 Second International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN). IEEE, 2016. http://dx.doi.org/10.1109/icrcicn.2016.7813542.

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Kaur, Sandy, and Eun Kyung Lee. "Diagnostic Analysis: Directional Relation Graph." In 2019 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2019. http://dx.doi.org/10.1109/cluster.2019.8891032.

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Laros, James H., Lee Ward, Ruth Klundt, Sue Kelly, James L. Tomkins, and Brian R. Kellogg. "Red storm IO performance analysis." In 2007 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2007. http://dx.doi.org/10.1109/clustr.2007.4629216.

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Duplyakin, Dmitry, Jed Brown, and Robert Ricci. "Active Learning in Performance Analysis." In 2016 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2016. http://dx.doi.org/10.1109/cluster.2016.63.

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Brunst, Nagel, and Malony. "A distributed performance analysis architecture for clusters." In Proceedings IEEE International Conference on Cluster Computing CLUSTR-03. IEEE, 2003. http://dx.doi.org/10.1109/clustr.2003.1253301.

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Corbalan, Julita, Lluis Alonso, Jordi Aneas, and Luigi Brochard. "Energy Optimization and Analysis with EAR." In 2020 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2020. http://dx.doi.org/10.1109/cluster49012.2020.00067.

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Reports on the topic "Cluster analysis"

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Kryzhanivs'kyi, Evstakhii, Liliana Horal, Iryna Perevozova, Vira Shyiko, Nataliia Mykytiuk, and Maria Berlous. Fuzzy cluster analysis of indicators for assessing the potential of recreational forest use. [б. в.], October 2020. http://dx.doi.org/10.31812/123456789/4470.

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Cluster analysis of the efficiency of the recreational forest use of the region by separate components of the recreational forest use potential is provided in the article. The main stages of the cluster analysis of the recreational forest use level based on the predetermined components were determined. Among the agglomerative methods of cluster analysis, intended for grouping and combining the objects of study, it is common to distinguish the three most common types: the hierarchical method or the method of tree clustering; the K-means Clustering Method and the two-step aggregation method. For the correct selection of clusters, a comparative analysis of several methods was performed: arithmetic mean ranks, hierarchical methods followed by dendrogram construction, K- means method, which refers to reference methods, in which the number of groups is specified by the user. The cluster analysis of forestries by twenty analytical grounds was not proved by analysis of variance, so the re-clustering of certain objects was carried out according to the nine most significant analytical features. As a result, the forestry was clustered into four clusters. The conducted cluster analysis with the use of different methods allows us to state that their combination helps to select reasonable groupings, clearly illustrate the clustering procedure and rank the obtained forestry clusters.
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Choudhary, Akok. Cluster-Based Repositories and Analysis. Fort Belvoir, VA: Defense Technical Information Center, May 2003. http://dx.doi.org/10.21236/ada420002.

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Wachtel, Amanda, Darryl Melander, and Olga Hart. ReNCAT: The Resilient Node Cluster Analysis Tool. Office of Scientific and Technical Information (OSTI), August 2022. http://dx.doi.org/10.2172/1880920.

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Monteiro, Joana, Beatriz Kury, and Angela Da Rocha. The Role of Flagship Firms, External Actors and Support Institutions in the Emergence of Successful Export Activities in Brazil: Two Industrial Cluster Studies. Inter-American Development Bank, September 2009. http://dx.doi.org/10.18235/0011331.

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This paper examines the process by which firms in a cluster start to export based on systemic interactions and the process of diffusion of exporting as a business strategy within the cluster. Two Brazilian manufacturing industries are studied, and within each one a geographic cluster was identified as the origin of dynamic export growth. Players in each industrial cluster, as well as other significant players, were interviewed or identified using secondary sources, and extensive secondary data research was undertaken to study clusters' historical development. Detailed analysis and a comparison of the two experiences made it possible to draw some general conclusions concerning the similarities and differences between the two clusters in terms of the adoption and diffusion of exporting.
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Weijters, Bert. Cluster Analysis in R: From Theory to Practice. Instats Inc., 2023. http://dx.doi.org/10.61700/3xjho79mx2fc0706.

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This Cluster Analysis in R workshop, led by professor Bert Weijters from Ghent University, provides participants with a comprehensive understanding of the theory and practice of cluster analysis, a crucial tool in academic research for identifying patterns within datasets, including datasets with large numbers of cases and/or variables. This hands-on workshop covers topics from a very brief introduction to RStudio and cluster analysis, to mastering different clustering techniques, and provides practical exercises on simulated and real-world datasets, equipping participants with valuable skills applicable in their own research.
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Anderson, Sandra. Eutamias minimus and E. amoenus : morphological cluster analysis. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.2262.

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Leland, Robert W. Dual mode use requirements analysis for the institutional cluster. Office of Scientific and Technical Information (OSTI), September 2003. http://dx.doi.org/10.2172/918226.

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Sturtevant, Judith E., Anand Ganti, Harold Edward Meyer, Joel O. Stevenson, Robert E. Benner, Jr, .), Susan Phelps Goudy, et al. Supercomputer and cluster performance modeling and analysis efforts:2004-2006. Office of Scientific and Technical Information (OSTI), February 2007. http://dx.doi.org/10.2172/903425.

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Sclove, Stanley L. Statistical Models and Methods for Cluster Analysis and Image Segmentation. Fort Belvoir, VA: Defense Technical Information Center, March 1986. http://dx.doi.org/10.21236/ada169145.

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Raykov, Tenko. Latent Class Analysis and Mixture Modeling. Instats Inc., 2023. http://dx.doi.org/10.61700/tkd5fah8evykd469.

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Latent class analysis (LCA) and mixture models (MM) are an applied statistical method for examining heterogeneity in studied populations. The method can be used to evaluate whether a studied population consists of an initially unknown number of several subpopulations (latent classes, types, clusters) that differ in important ways. This workshop introduces participants to the general field of classification (clustering), using LCA as a model-based version of cluster analysis and moving on to more general mixture modeling with latent variables. Hands-on examples with best practices for analysis and inference are used throughout in the popular program Mplus. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, the seminar offers 2 ECTS Equivalent points.
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