Dissertations / Theses on the topic 'Cluster analysis'
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Kozovska, Kornelia <1981>. "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.
Full textKozovska, Kornelia <1981>. "Business Clusters in Eastern Europe: Policy Analysis and Cluster Performance." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2009. http://amsdottorato.unibo.it/1611/.
Full textPavlou, 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/.
Full textNgai, 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.
Full textDufour, Alyssa Beth. "Cluster analysis of longitudinal trajectories." Thesis, Boston University, 2013. https://hdl.handle.net/2144/12751.
Full textCluster 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.
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.
Full textTakahashi, Atsushi. "Hierarchical Cluster Analysis of Dense GNSS Data and Interpretation of Cluster Characteristics." Kyoto University, 2019. http://hdl.handle.net/2433/244510.
Full textPopescu, 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.
Full textCuciti, Virginia <1989>. "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.
Full textBozkirlioglu, 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.
Full textcommon 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.
Busse, Ludwig M. Orbanz Peter Buhmann Joachim M. Buhmann Joachim M. Buhmann Joachim M. "Cluster analysis of heterogeneous rank data." Zurich : ETH Department of Computer Science, Institute of Computational Sciences, 2007. http://e-collection.ethbib.ethz.ch/show?type=dipl&nr=350.
Full textMolin, Felix. "Cluster analysis of European banking data." Thesis, KTH, Matematisk statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-219597.
Full textKreditinstituten utgör en central del av livet som det ser ut idag och har gjort det under en lång tid. Ett fel inom banksystemet kan orsaka enorma skador för individer likväl som länder. Ett nutida och minnesvärt fel är den globala finanskrisen 2007-2009. Den har påverkat millioner människor på olika vis ända sedan den slog till. Vad som orsakade den är en komplex fråga som inte kan besvaras med lätthet. Men vad har gjorts för att förebygga att något liknande händer igen? Hur har affärsmodellerna för kreditinstituten ändrats sedan krisen? Klusteranalys används i denna rapport för att adressera dessa frågor. Bankdata processerades med Calinski-Harabasz Kriteriet and Wards metod och detta resulterade i att två kluster hittades. Ett kluster är en samling observationer med liknande karakteristik eller affärsmodell i detta fall. De affärsmodeller som klustrena representerar är universella banker med retail fokus samt universella banker med wholessale fokus. Dessa affärsmodeller har analyserats över tid, vilket har avslöjat att kreditinstituten har utvecklats i en hälsosam riktning. Kreditinstituten var mer finansiellt pålitliga 2016 jämfört med 2007. Enligt trender i datan så är det troligt att denna utveckling forsätter.
Brolin, Morgan, and Erik Ledin. "Detecting trolls on twitterthrough cluster analysis." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-208354.
Full textDen sociala nätverkstjänsten Twitter är utformad för att låta användare effektivt och snabbtsprida information via korta meddelanden som sänds ut till världen. Denna typ av effektivaspridning av information som inte kontrolleras eller redigeras bär med sig problem i formenav spridning av misinformation och annan skadlig aktivitet, då det kan vara mycket svårt attsäkerställa vilken information som är pålitlig. Denna studie försöker klargöra dessa problemoch ta reda på om det är möjligt att identifiera dessa skadliga användare genom att filtreratweets på nyckelord, klustra dessa tweets baserat på likhet och analysera klustren isamband med användardata såsom antal följare, antal konton följda och att geolocation äravstängt. Tweetsen hämtades med hjälp av Twitters streaming API och klustringen gjordesmed tf-idf k-means clustering. Uppskattningsvis 2000 tweets hämtades för varje nyckelord,och cirka 4000 ofiltrerade tweets, för att möjliggöra att skilja på vilka ämnen som har störreoch mindre andelar potentiellt skadliga användare. Resultaten visar på att politiska ochkontroversiella ämnen såsom “ISIS”, “Ryssland” och “Putin” har märkbart högre andelarpotentiellt skadliga användare, jämfört med tweets som inte filtrerats baserat på någotnyckelord, vilka i sin tur har högre andelar än mer neutrala nyckelord såsom “cat”, “happy”och “car”. Resultaten tyder på att det är svårt att enbart använda klustring för att hittaskadliga användare och att analysen av användardata inte alltid visar den hela bilden ochkan ge felaktiga resultat åt båda håll. Trots det kan klustring i kombination med andratekniker såsom data analys användas för att analysera hur skadliga användare är spriddagenom olika ämnen på twitter.
Dimitrakopoulou, Vasiliki. "Bayesian variable selection in cluster analysis." Thesis, University of Kent, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.594195.
Full textJarvis, C. "Spatial analysis of cluster randomised trials." Thesis, London School of Hygiene and Tropical Medicine (University of London), 2018. http://researchonline.lshtm.ac.uk/4648971/.
Full textCorreia, Maria Inês Costa. "Cluster analysis of financial time series." Master's thesis, Instituto Superior de Economia e Gestão, 2020. http://hdl.handle.net/10400.5/21016.
Full textEsta dissertação aplica o método da Signature como medida de similaridade entre dois objetos de séries temporais usando as propriedades de ordem 2 da Signature e aplicando-as a um método de Clustering Asimétrico. O método é comparado com uma abordagem de Clustering mais tradicional, onde a similaridade é medida usando Dynamic Time Warping, desenvolvido para trabalhar com séries temporais. O intuito é considerar a abordagem tradicional como benchmark e compará-la ao método da Signature através do tempo de computação, desempenho e algumas aplicações. Estes métodos são aplicados num conjunto de dados de séries temporais financeiras de Fundos Mútuos do Luxemburgo. Após a revisão da literatura, apresentamos o método Dynamic Time Warping e o método da Signature. Prossegue-se com a explicação das abordagens de Clustering Tradicional, nomeadamente k-Means, e Clustering Espectral Assimétrico, nomeadamente k-Axes, desenvolvido por Atev (2011). O último capítulo é dedicado à Investigação Prática onde os métodos anteriores são aplicados ao conjunto de dados. Os resultados confirmam que o método da Signature têm efectivamente potencial para Machine Learning e previsão, como sugerido por Levin, Lyons and Ni (2013).
This thesis applies the Signature method as a measurement of similarities between two time-series objects, using the Signature properties of order 2, and its application to Asymmetric Spectral Clustering. The method is compared with a more Traditional Clustering approach where similarities are measured using Dynamic Time Warping, developed to work with time-series data. The intention for this is to consider the traditional approach as a benchmark and compare it to the Signature method through computation times, performance, and applications. These methods are applied to a financial time series data set of Mutual Exchange Funds from Luxembourg. After the literature review, we introduce the Dynamic Time Warping method and the Signature method. We continue with the explanation of Traditional Clustering approaches, namely k-Means, and Asymmetric Clustering techniques, namely the k-Axes algorithm, developed by Atev (2011). The last chapter is dedicated to Practical Research where the previous methods are applied to the data set. Results confirm that the Signature method has indeed potential for machine learning and prediction, as suggested by Levin, Lyons, and Ni (2013).
info:eu-repo/semantics/publishedVersion
Donnelly, Julie A. "Subtypes of autism by cluster analysis /." free to MU campus, to others for purchase, 1996. http://wwwlib.umi.com/cr/mo/fullcit?p9737864.
Full textYeung, Ka Yee. "Cluster analysis of gene expression data /." Thesis, Connect to this title online; UW restricted, 2001. http://hdl.handle.net/1773/6986.
Full textSantiago, Calderón José Bayoán. "On Cluster Robust Models." Scholarship @ Claremont, 2019. https://scholarship.claremont.edu/cgu_etd/132.
Full textWhite, Ceri. "Cluster analysis : algorithms, hazards and small area relative survival." Thesis, University of South Wales, 2008. https://pure.southwales.ac.uk/en/studentthesis/cluster-analysis(b799eddf-4d11-4cd2-9cd0-3d0480dcaedd).html.
Full textSoon, Shih Chung. "On detection of extreme data points in cluster analysis." Connect to resource, 1987. http://rave.ohiolink.edu/etdc/view.cgi?acc%5Fnum=osu1262886219.
Full textHolm, Rasmus. "Cluster Analysis of Discussions on Internet Forums." Thesis, Linköpings universitet, Artificiell intelligens och integrerad datorsystem, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-129934.
Full textWindridge, David. "A fluctuation analysis for optical cluster galaxies." Thesis, University of Bristol, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.302173.
Full textVaughan, Carol E. "A cluster analysis method for materials selection." Thesis, Virginia Tech, 1992. http://hdl.handle.net/10919/41497.
Full textMaster of Science
Єфіменко, Тетяна Михайлівна, Татьяна Михайловна Ефименко, Tetiana Mykhailivna Yefimenko, Олена Владиславівна Коробченко, Елена Владиславовна Коробченко, and Olena Vladyslavivna Korobchenko. "Informational Extreme Cluster Analysis of Input Data." Thesis, Sumy State University, 2016. http://essuir.sumdu.edu.ua/handle/123456789/47076.
Full textSullivan, Terry. "The Cluster Hypothesis: A Visual/Statistical Analysis." Thesis, University of North Texas, 2000. https://digital.library.unt.edu/ark:/67531/metadc2444/.
Full textLin, Dong. "Model-based cluster analysis using Bayesian techniques." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2008. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.
Full textLee, Jong-Seok. "Preserving nearest neighbor consistency in cluster analysis." [Ames, Iowa : Iowa State University], 2009. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3369852.
Full textLee, Dong-Gwi. "A cluster analysis of procrastination and coping /." free to MU campus, to others for purchase, 2003. http://wwwlib.umi.com/cr/mo/fullcit?p3100057.
Full textALBUQUERQUE, Mácio Augusto de. "Estabilidade em análise de agrupamento (cluster analysis)." Universidade Federal Rural de Pernambuco, 2005. http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/5178.
Full textMade available in DSpace on 2016-08-03T17:35:12Z (GMT). No. of bitstreams: 1 Macio Augusto de Albuquerque.pdf: 1005283 bytes, checksum: b9e55eee4b0b853629358e6b2158ba81 (MD5) Previous issue date: 2005-02-23
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES
The main objective of this research was to propose a systematic to the study and interpretation of the stability of methods in cluster analysis through many cluster algorithms in vegetation data. The data set used came from a survey in the Silviculture Forest at Federal University of Viçosa – MG. To perform the cluster analysis the matrices of Mahalanobis distance were estimated based on the original data and by “bootstrap” resampling. Also the methods of single linkageage, complete linkageage, the average of the distances, the centroid, the medium and the Ward were used. For the detection of the association among the methods it was applied the chi-square test. For the various methods of clustering it was obtained a cofenetical correlation. The results of the associations of methods were very similar, indicating, in principle, that any algorithm of cluster studied is stabilized and exist, in fact, groups among the individuals analyzed. However, it was concluded that themethods coincide with themselves, except the methods of centroid and Ward. Also the centroid methods and average when compared to the Ward, respectively, based on the matrices of Mahalanobis starting from the original data set and “bootstrap”. The methodology proposed is promising to the study and interpretation of the stabilityof methods concerning the cluster analysis in vegetation data.
Objetivou-se propor uma sistemática para o estudo e a interpretação da estabilidade dos métodos em análise de agrupamento, através de vários algoritmos de agrupamento em dados de vegetação. Utilizou-se dados provenientes de um levantamento na Mata da Silvicultura, da Universidade Federal de Viçosa-MG. Para análise de agrupamento foram estimadas as matrizes de distância de Mahalanobis com base nos dados originais e via reamostragem “bootstrap” e aplicados os métodos da ligação simples, ligação completa, médias das distâncias, do centróide, da mediana e do Ward. Para a detecção de associação entre os métodos foi aplicado o teste qui-quadrado. Para os diversos métodos de agrupamento foi obtida a correlação cofenética. Os resultados de associação dos métodos foram semelhantes, indicando em princípio que qualquer algoritmo de agrupamento estudado está estabilizado e existem, de fato, grupos entre os indivíduos observados. No entanto, observou-se que os métodos são coincidentes, exceto osmétodos do centróide e Ward e os métodos do centróide e mediana quando comparados com o de Ward, respectivamente, com base nas matrizes de Mahalanobis a partir dos dados originais e “bootstrap”. A sistemática proposta é promissora para o estudo e a interpretação da estabilidade dos métodos de análise de agrupamento em dados de vegetação.
Parker, Brandon S. "CLUE: A Cluster Evaluation Tool." Thesis, University of North Texas, 2006. https://digital.library.unt.edu/ark:/67531/metadc5444/.
Full textStrehl, Alexander. "Relationship-based clustering and cluster ensembles for high-dimensional data mining." Thesis, Full text (PDF) from UMI/Dissertation Abstracts International, 2002. http://wwwlib.umi.com/cr/utexas/fullcit?p3088578.
Full textLi, Hao. "Feature cluster selection for high-dimensional data analysis." Diss., Online access via UMI:, 2007.
Find full textWang, Dali. "Adaptive Double Self-Organizing Map for Clustering Gene Expression Data." Fogler Library, University of Maine, 2003. http://www.library.umaine.edu/theses/pdf/WangD2003.pdf.
Full textZhan, Cheng Juan. "An Alternative Approach to Visualizing Stock Market Correlation Matrices- An Empirical study of forming portfolios that contain only small numbers of stocks using both existing and newly discovered visualization methods." Thesis, University of Canterbury. Economics and Finance, 2014. http://hdl.handle.net/10092/9649.
Full textChan, Alton Kam Fai. "Hyperplane based efficient clustering and searching /." View abstract or full-text, 2003. http://library.ust.hk/cgi/db/thesis.pl?ELEC%202003%20CHANA.
Full textTantrum, Jeremy. "Model based and hybrid clustering of large datasets /." Thesis, Connect to this title online; UW restricted, 2003. http://hdl.handle.net/1773/8933.
Full textSpringuel, R. Padraic. "Applying Cluster Analysis to Physics Education Research Data." Fogler Library, University of Maine, 2010. http://www.library.umaine.edu/theses/pdf/SpringuelRP2010.pdf.
Full textErtugrul, Hamza Oguz. "Determination Of Weak Transmission Links By Cluster Analysis." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/3/12611236/index.pdf.
Full textAleksakhin, Vladyslav. "Visualization of gene ontology and cluster analysis results." Thesis, Linnéuniversitetet, Institutionen för datavetenskap, fysik och matematik, DFM, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-21248.
Full textHegazy, Yasser Ali. "Delineating geostratigraphy by cluster analysis of piezocone data." Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/20506.
Full textŁuksza, Marta [Verfasser]. "Cluster statistics and gene expression analysis / Marta Łuksza." Berlin : Freie Universität Berlin, 2012. http://d-nb.info/1026883113/34.
Full textHolmes, Rebecca Jane. "Analysis of a novel cluster of imprinted genes." Thesis, University of Oxford, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.270370.
Full textTse, Wai-hin Kenneth, and 謝維軒. "Forensic analysis using FAT32 file cluster allocation patterns." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B46605733.
Full textVan, Der Linde Byron-Mahieu. "A comparative analysis of the singer’s formant cluster." Thesis, Stellenbosch : Stellenbosch University, 2013. http://hdl.handle.net/10019.1/85563.
Full textENGLISH ABSTRACT: It is widely accepted that the singer’s formant cluster (Fs) – perceptual correlates being twang and ring, and pedagogically referred to as head resonance – is the defining trait of a classically trained voice. Research has shown that the spectral energy a singer harnesses in the Fs region can be measured quantitatively using spectral indicators Short-Term Energy Ratio (STER) and Singing Power Ratio (SPR). STER is a modified version of the standard measurement tool Energy Ratio (ER) that repudiates dependency on the Long-Term Average Spectrum (LTAS). Previous studies have shown that professional singers produce more Fs spectral energy when singing in ensemble mode than in solo mode; however for amateur singers, the opposite trend was noticed. Little empirical evidence in this regard is available concerning undergraduate vocal performance majors. This study was aimed at investigating the resonance tendencies of individuals from the latter target group, as evidenced when singing in two performance modes: ensemble and solo. Eight voice students (two per SATB voice part) were selected to participate. Subjects were recorded singing their parts individually, as well as in full ensemble. By mixing the solo recordings together, comparisons of the spectral content could be drawn between the solo and ensemble performance modes. Samples (n=4) were extracted from each piece for spectral analyses. STER and SPR means were highly proportional for both pieces. Results indicate that the singers produce significantly higher levels of spectral energy in the Fs region in ensemble mode than in solo mode for one piece (p<0.05), whereas findings for the other piece were insignificant. The findings of this study could inform the pedagogical approach to voice-training, and provides empirical bases for discussions about voice students’ participation in ensemble ventures.
AFRIKAANSE OPSOMMING: Dit word algemeen aanvaar dat die singer’s formant cluster (Fs) – die perseptuele korrelate is die Engelse “twang” en “ring”, en waarna daar in die pedagogie verwys word as kopresonansie – die bepalende eienskap is van ’n Klassiek-opgeleide stem. Navorsing dui daarop dat die spektrale energie wat ’n sanger in die Fs omgewing inspan kwantitatief gemeet kan word deur die gebruik van Short-Term Energy Ratio (STER) en Singing Power Ratio (SPR) as spektrale aanwysers. STER is ’n gewysigde weergawe van die standaard maatstaf vir energie in die Fs, naamlik Energy Ratio (ER), wat afhanklikheid van die Long-Term Average Spectrum (LTAS) verwerp. Vorige studies het getoon dat professionele sangers meer Fs energie produseer in ensemble konteks as in solo konteks, in teenstelling met amateur sangers waar die teenoorgestelde die norm is. Min empiriese data in hierdie verband is beskikbaar, m.b.t. voorgraadse uitvoerende sangstudente. Hierdie studie is daarop gemik om die tendense in resonansie by individue uit die laasgenoemde groep te ondersoek, soos dit blyk in die twee uitvoerende kontekste: ensemble en solo. Agt sangstudente (twee per SATB stemgroep) is geselekteer om aan die studie deel te neem. Die deelnemers het hul stempartye individueel en in volle ensemble gesing, en is by beide geleenthede opgeneem. Deur die soloopnames te meng, kon vergelykings van die spektrale inhoud gemaak word tussen die solo en ensemble konteks. ’n Steekproef (n=4) is uit elke stuk onttrek vir spektrale analise. Die STER en SPR gemiddeldes was eweredig vir beide stukke. Resultate toon dat die sangers beduidend hoër vlakke van spektrale energie in die Fs omgewing produseer in ensemble konteks as in solo konteks vir een stuk (p<0.05), terwyl die bevindinge vir die tweede stuk nie beduidend was nie. Die bevindinge van hierdie studie kan belangrik wees vir die pedagogiese benadering tot stemopleiding, en lewer empiriese basis vir gesprekke oor die betrokkenheid van sangstudente in die ensemble bedryf.
Farahi, Arya, August E. Evrard, Eduardo Rozo, Eli S. Rykoff, and Risa H. Wechsler. "Galaxy cluster mass estimation from stacked spectroscopic analysis." OXFORD UNIV PRESS, 2016. http://hdl.handle.net/10150/621426.
Full textDinsmore, Kimberly. "Factor and Cluster Analysis of Learning Orientation Questionnaire." Digital Commons @ East Tennessee State University, 2018. https://dc.etsu.edu/asrf/2018/schedule/103.
Full textStephens, Chad Louis. "Autonomic Differentiation of Emotions: A Cluster Analysis Approach." Thesis, Virginia Tech, 2007. http://hdl.handle.net/10919/79690.
Full textMaster of Science
[Appendix B: Beck Depression Inventory, p. 61-64, was removed Oct. 4, 2011 GMc]
Dasah, Julius Berry. "Estimating the number of clusters in cluster analysis." 2006. http://www.lib.ncsu.edu/theses/available/etd-11082006-102315/unrestricted/etd.pdf.
Full textΚαράγεωργα, Ισμήνη. "Ανάλυση συστάδων (cluster analysis)." Thesis, 2012. http://hdl.handle.net/10889/5932.
Full textIn the current diplomatic thesis is analyzed the problem of cluster analysis. The purpose of cluster analysis is to group items in clusters, so that items belonging to the same cluster have a greater similarity than the items belonging to different clusters.