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1

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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5

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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6

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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7

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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11

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.

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12

Molin, Felix. "Cluster analysis of European banking data." Thesis, KTH, Matematisk statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-219597.

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Credit institutions constitute a central part of life as it is today and has been doing so for a long time. A fault within the banking system can cause a tremendous amount of damage to individuals as well as countries. A recent and memorable fault is the global financial crisis 2007-2009. It has affected millions of people in different ways ever since it struck. What caused it is a complex issue which cannot be answered easily. But what has been done to prevent something similar to occur once again? How has the business models of the credit institutions changed since the crisis? Cluster analysis is used in this thesis to address these questions. Banking-data were processed with Calinski-Harabasz Criterion and Ward's method and this resulted in two clusters being found. A cluster is a collection of observations that have similar characteristics or business model in this case. The business models that the clusters represents are universal banking with a retail focus and universal banking with a wholesale focus. These business models have been analyzed over time (2007-2016), which revealed that the credit institutions have developed in a healthy direction. Thus, credit institutions were more financially reliable in 2016 compared to 2007. According to trends in the data this development is likely to continue.
Kreditinstituten 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.
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13

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.

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The social media platform Twitter is designed to allow users to efficiently spread informationthrough short messages that are broadcast to the world. The efficient way to spreadinformation that is in no way controlled or edited brings inherent problems with the spreadingof misinformation and other malicious activity as it can often be very difficult to establishwhat information can be considered reliable. This study seeks to showcase these problemsas well as find out whether it is possible to identify these malicious users by filtering tweetsby keywords, clustering the tweets based on similarity and analyzing these clusters alongwith user data such as amount of followers, number of accounts followed, and geolocationbeing turned off. The tweets were gathered using the Twitter streaming API and theclustering was done through the use of k-means clustering using a tf-idf approach.Approximately 2000 tweets were gathered for every keyword, and roughly 4000 using nofilter, to allow us to discern which topics contain higher and lower percentages of likely trollsor malicious users. The results show that highly political and controversial topics such as“ISIS”, “Russia”, and “Putin” have significantly higher percentages of likely trolls andmalicious users when compared to tweets that are not filtered by any keyword, which in turnhas higher amounts than more neutral keywords such as “cat”, “happy” and “car”. Howeverthe results also show that it would be very difficult to use clustering alone to find trolls ormalicious users, and that the analysis of user data does not paint a complete picture andmay give both false positives as well as false negatives. However clustering in combinationwith other techniques such as user data analysis can be used to successfully analyze howmalicious users are spread through different topics on Twitter.
Den 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.
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14

Dimitrakopoulou, Vasiliki. "Bayesian variable selection in cluster analysis." Thesis, University of Kent, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.594195.

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Statistical analysis of data sets of high-dimensionality has met great interest over the past years, with great applications on disciplines such as medicine, nellascience, pattern recognition, image analysis and many others. The vast number of available variables though, contrary to the limited sample size, often mask the cluster structure of the data. It is often that some variables do not help in distinguishing the different clusters in the data; patterns over the samp•.l ed observations are, thus, usually confined to a small subset of variables. We are therefore interested in identifying the variables that best discriminate the sample, simultaneously to recovering the actual cluster structure of the objects under study. With the Markov Chain Monte Carlo methodology being widely established, we investigate the performance of the combined tasks of variable selection and clustering procedure within the Bayesian framework. Motivated by the work of Tadesse et al. (2005), we identify the set of discriminating variables with the use of a latent vector and form the clustering procedure within the finite mixture models methodology. Using Markov chains we draw inference on, not just the set of selected variables and the cluster allocations, but also on the actual number of components: using the f:teversible Jump MCMC sampler (Green, 1995) and a variation of t he SAMS sampler of Dahl (2005). However, sensitivity t o the hyperparameters settings of the covariance structure of the suggested model motivated our interest in an Empirical Bayes procedure to pre-specify the crucial hyper parameters. Further on addressing the problem of II ~----. -- 1 hyperparameters' sensitivity, we suggest several different covariance structures for the mixture components. Developing MATLAB codes for all models introduced in this thesis, we apply and compare the various models suggested on a set of simulated data, as well as on three real data sets; the iris, the crabs and the arthritis data sets.
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15

Jarvis, 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/.

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Cluster randomised trials (CRTs) often use geographical areas as the unit of randomisation. Despite this, explicit consideration of the location and spatial distribution of observations is rare. In many trials, the location of participants will have little importance, however in some, especially against infectious diseases, spillover effects due to participants being located close together may affect trial results. This PhD takes a multidisciplinary approach to apply and evaluate spatial analysis methods in CRTs, furthering understanding of how spatial analysis can complement traditional evaluation of CRTs. I began by conducting a systematic review of CRTs that used spatial analysis techniques. I found only 10 published papers, most of which being supplementary analyses of the main trial. I then conducted a spatial analysis of an Oral Polio Vaccine (OPV) transmission household CRT. This provided additional insights into the underlying mechanism of polio transmission that support the global cessation of OPV and emphasises the difficulties of the global eradication of polio. Following this, I performed a spatial reanalysis of an insecticide-treated bed net CRT, applying approaches from the systematic review and a new method I developed called cluster reallocation to assess the presence and impact of spatial spillover in the trial. This analysis confirmed the previous estimate of intervention effect while showing evidence of a spillover effect. I carried out simulation studies to evaluate the impact of spillover and spatial effects on the standard CRT model and compared spatial regression to non-spatial models. These simulations focus on how to generate spatial spillover effects and the magnitude needed before spatial consideration becomes important to CRTs. I found that non-spatial CRT models are relatively robust to spatial effects and that the use of spatial models does not appear to improve upon the non-spatial model. The collective findings of this thesis highlight that standard CRT approaches are typically robust to small scale spillover effects and consideration of the spatial distribution of observations appears to provide little utility in the main analysis of a trial. Despite this, spatial methods can provide additional insights into the mechanism of interventions and are well suited to secondary analyses of CRTs, especially with the increasing collection of GPS data in CRTs.
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16

Correia, 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.

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Mestrado em Mathematical Finance
Esta 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
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17

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.

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18

Yeung, Ka Yee. "Cluster analysis of gene expression data /." Thesis, Connect to this title online; UW restricted, 2001. http://hdl.handle.net/1773/6986.

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19

Santiago, Calderón José Bayoán. "On Cluster Robust Models." Scholarship @ Claremont, 2019. https://scholarship.claremont.edu/cgu_etd/132.

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Cluster robust models are a kind of statistical models that attempt to estimate parameters considering potential heterogeneity in treatment effects. Absent heterogeneity in treatment effects, the partial and average treatment effect are the same. When heterogeneity in treatment effects occurs, the average treatment effect is a function of the various partial treatment effects and the composition of the population of interest. The first chapter explores the performance of common estimators as a function of the presence of heterogeneity in treatment effects and other characteristics that may influence their performance for estimating average treatment effects. The second chapter examines various approaches to evaluating and improving cluster structures as a way to obtain cluster-robust models. Both chapters are intended to be useful to practitioners as a how-to guide to examine and think about their applications and relevant factors. Empirical examples are provided to illustrate theoretical results, showcase potential tools, and communicate a suggested thought process. The third chapter relates to an open-source statistical software package for the Julia language. The content includes a description for the software functionality and technical elements. In addition, it features a critique and suggestions for statistical software development and the Julia ecosystem. These comments come from my experience throughout the development process of the package and related activities as an open-source and professional software developer. One goal of the paper is to make econometrics more accessible not only through accessibility to functionality, but understanding of the code, mathematics, and transparency in implementations.
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White, 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.

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This thesis presents research that has demonstrated the use of clustering algorithms in the analysis of datasets routinely collected by cancer registries. This involved a review of existing algorithms and their application in studies of spatial and temporal variations in cancer rates. As a result of continuing public and scientific concern there has been an increase in the numbers of cancer related enquiries in recent years that has helped to raise the profile of the work of cancer registries. There are no official guidelines on the approach to be taken in such studies in relation to cluster analysis. In this study, a variety of cluster algorithms were applied to leukaemia data collected by the Welsh Cancer Intelligence and Surveillance Unit in order to propose an approach that could be adopted in future investigations of cancer incidence in Wales. For example, different methodologies have been employed to determine if an excess risk occurs near hazardous sources and one of the studies in the portfolio compares the results of using three methods to determine if an increased risk of cancer occurs in the vicinity of landfill sites and electric power lines. This uses new digital products that permit a more detailed estimation of the population at risk and permit a sensitivity analysis of the results of such investigations. In the third portfolio, analysis of relative survival at small area level has been made possible using a new level of geographical resolution that has recently been released in the United Kingdom. This study shows the benefits of using this new level of geography for small area studies of cancer survival where there are generally small numbers of deaths per spatial unit. It is anticipated that together these research studies will be of wider benefit to other registries in the UK charged with investigating spatial and temporal variations in cancer rates.
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Soon, 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.

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Holm, 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.

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The growth of textual content on internet forums over the last decade have been immense which have resulted in users struggling to find relevant information in a convenient and quick way. The activity of finding information from large data collections is known as information retrieval and many tools and techniques have been developed to tackle common problems. Cluster analysis is a technique for grouping similar objects into smaller groups (clusters) such that the objects within a cluster are more similar than objects between clusters. We have investigated the clustering algorithms, Graclus and Non-Exhaustive Overlapping k-means (NEO-k-means), on textual data taken from Reddit, a social network service. One of the difficulties with the aforementioned algorithms is that both have an input parameter controlling how many clusters to find. We have used a greedy modularity maximization algorithm in order to estimate the number of clusters that exist in discussion threads. We have shown that it is possible to find subtopics within discussions and that in terms of execution time, Graclus has a clear advantage over NEO-k-means.
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Windridge, David. "A fluctuation analysis for optical cluster galaxies." Thesis, University of Bristol, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.302173.

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Vaughan, Carol E. "A cluster analysis method for materials selection." Thesis, Virginia Tech, 1992. http://hdl.handle.net/10919/41497.

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Materials have typically been selected based on the familiarities and past experiences of a limited number of designers with a limited number of materials. Problems arise when the designer is unfamiliar with new or improved materials, or production processes more efficient and economical than past choices. Proper utilization of complete materials and processing information would require acquisition, understanding, and manipulation of huge amounts of data, including dependencies among variables and "what-if" situations. The problem of materials selection has been addressed with a variety of techniques, from simple broad-based heuristics as guidelines for selection, to elaborate expert system technologies for specific selection situations. However, most materials selection methodologies concentrate only on material properties, leaving other decision criteria with secondary importance. Factors such as component service environment, design features, and feasible manufacturing methods directly influence the material choice, but are seldom addressed in systematic materials selection procedures. This research addresses the problem of developing a systematic materials selection procedure that can be integrated with standard materials data bases. The three-phase methodology developed utilizes a group technology code and cluster analysis method for the selection. The first phase is of go/no go nature, and utilizes the possible service environment requirements of ferromagnetism and chemical corrosion resistance to eliminate materials from candidacy. In the second phase, a cluster analysis is performed on key design and manufacturing attributes captured in a group technology code for remaining materials. The final phase of the methodology is user-driven, in which further analysis of the output of the cluster analysis can be performed for more specific or subjective attributes.
Master of Science
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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.

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The categorical model and decision support system learning algorithm are considered in the article. Proposed algorithm allows to create decision support system, which is functioning in a clusteranalysis state. Synthesis of the decision support system is based on maximization of informational system ability due to making additional information restrictions in the learning process.
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Sullivan, Terry. "The Cluster Hypothesis: A Visual/Statistical Analysis." Thesis, University of North Texas, 2000. https://digital.library.unt.edu/ark:/67531/metadc2444/.

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By allowing judgments based on a small number of exemplar documents to be applied to a larger number of unexamined documents, clustered presentation of search results represents an intuitively attractive possibility for reducing the cognitive resource demands on human users of information retrieval systems. However, clustered presentation of search results is sensible only to the extent that naturally occurring similarity relationships among documents correspond to topically coherent clusters. The Cluster Hypothesis posits just such a systematic relationship between document similarity and topical relevance. To date, experimental validation of the Cluster Hypothesis has proved problematic, with collection-specific results both supporting and failing to support this fundamental theoretical postulate. The present study consists of two computational information visualization experiments, representing a two-tiered test of the Cluster Hypothesis under adverse conditions. Both experiments rely on multidimensionally scaled representations of interdocument similarity matrices. Experiment 1 is a term-reduction condition, in which descriptive titles are extracted from Associated Press news stories drawn from the TREC information retrieval test collection. The clustering behavior of these titles is compared to the behavior of the corresponding full text via statistical analysis of the visual characteristics of a two-dimensional similarity map. Experiment 2 is a dimensionality reduction condition, in which inter-item similarity coefficients for full text documents are scaled into a single dimension and then rendered as a two-dimensional visualization; the clustering behavior of relevant documents within these unidimensionally scaled representations is examined via visual and statistical methods. Taken as a whole, results of both experiments lend strong though not unqualified support to the Cluster Hypothesis. In Experiment 1, semantically meaningful 6.6-word document surrogates systematically conform to the predictions of the Cluster Hypothesis. In Experiment 2, the majority of the unidimensionally scaled datasets exhibit a marked nonuniformity of distribution of relevant documents, further supporting the Cluster Hypothesis. Results of the two experiments are profoundly question-specific. Post hoc analyses suggest that it may be possible to predict the success of clustered searching based on the lexical characteristics of users' natural-language expression of their information need.
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Lin, 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.

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Lee, 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.

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Lee, 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.

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ALBUQUERQUE, 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.

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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.
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Parker, Brandon S. "CLUE: A Cluster Evaluation Tool." Thesis, University of North Texas, 2006. https://digital.library.unt.edu/ark:/67531/metadc5444/.

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Modern high performance computing is dependent on parallel processing systems. Most current benchmarks reveal only the high level computational throughput metrics, which may be sufficient for single processor systems, but can lead to a misrepresentation of true system capability for parallel systems. A new benchmark is therefore proposed. CLUE (Cluster Evaluator) uses a cellular automata algorithm to evaluate the scalability of parallel processing machines. The benchmark also uses algorithmic variations to evaluate individual system components' impact on the overall serial fraction and efficiency. CLUE is not a replacement for other performance-centric benchmarks, but rather shows the scalability of a system and provides metrics to reveal where one can improve overall performance. CLUE is a new benchmark which demonstrates a better comparison among different parallel systems than existing benchmarks and can diagnose where a particular parallel system can be optimized.
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Strehl, 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.

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Li, Hao. "Feature cluster selection for high-dimensional data analysis." Diss., Online access via UMI:, 2007.

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Wang, 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.

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Zhan, 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.

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The core of stock portfolio diversification is to pick stocks from different correlation clusters when forming portfolios. The result is that the chosen stocks will be only weakly correlated with each other. However, since correlation matrices are high dimensional, it is close to impossible to determine correlation clusters by simply looking at a correlation matrix. It is therefore common to regard industry groups as correlation clusters. In this thesis, we used three visualization methods namely Hierarchical Cluster Trees, Minimum Spanning Trees and neighbor-Net splits graphs to “collapse” correlation matrices’ high dimensional structures onto two-dimensional planes, and then assign stocks into different clusters to create the correlation clusters. We then simulated sets of portfolios where each set contains 1000 portfolios, and stocks in each of the portfolio were picked from the correlation clusters suggested by each of the three visualization methods and industry groups (another way of determine correlation clusters). The mean and variance distribution of each set of 1000 simulated portfolios gives us an indication of how well those clusters were determined. The examinations were conducted on two sets of financial data. The first one is the 30 stocks in the Dow Jones Industrial average which contains relatively small number of stocks and the second one is the ASX 200 which contains relatively larger number of stocks. We found none of the methods studied consistently defined correlation clusters more efficiently than others in out-of-sample testing. The thesis does contribute the finance literature in two ways. Firstly, it introduces the neighbor-Net method as an alternative way to visualize financial data’s underlying structures. Secondly, it used a novel “visualization
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Chan, 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.

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Tantrum, Jeremy. "Model based and hybrid clustering of large datasets /." Thesis, Connect to this title online; UW restricted, 2003. http://hdl.handle.net/1773/8933.

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Springuel, 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.

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Ertugrul, 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.

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Due to faults and switching, transmission lines encounter power oscillations referred as power swings. Although in most cases they do not lead to an eventual instability, severe changes in power flows on the lines may cause the operation of impedance relays incorrectly, leading to cascaded tripping of other lines. Out-of-Step tripping function is employed in modern distance relays to distinguish such an unstable swing but setting the parameters and deciding lines to be tripped require detailed dynamic power system modelling and analysis. The proposed method aims to determine possible out-of-step (OOS) locations on a power system without performing detailed dynamic simulations. Method presented here, is based on grouping of the buses by statistical clustering analysis of the network impedance matrix. Inter-cluster lines are shown to be more vulnerable to give rise to OOS as proven with dynamic simulations on IEEE 39 bus test system.
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Aleksakhin, 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.

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The purpose of the thesis is to develop a new visualization method for Gene Ontologiesand hierarchical clustering. These are both important tools in biology andmedicine to study high-throughput data such as transcriptomics and metabolomicsdata. Enrichment of ontology terms in the data is used to identify statistically overrepresentedontology terms, that give insight into relevant biological processes orfunctional modules. Hierarchical clustering is a standard method to analyze andvisualize data to nd relatively homogeneous clusters of experimental data points.Both methods support the analysis of the same data set, but are usually consideredindependently. However, often a combined view such as: visualizing a large data setin the context of an ontology under consideration of a clustering of the data.The result of the current work is a user-friendly program that combines twodi erent views for analysing Gene Ontology and Cluster simultaneously. To makeexplorations of such a big data possible we developed new visualization approach.
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Hegazy, Yasser Ali. "Delineating geostratigraphy by cluster analysis of piezocone data." Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/20506.

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Łuksza, Marta [Verfasser]. "Cluster statistics and gene expression analysis / Marta Łuksza." Berlin : Freie Universität Berlin, 2012. http://d-nb.info/1026883113/34.

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43

Holmes, 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.

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Tse, 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.

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45

Van, 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.

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Thesis (MMus)-- Stellenbosch University, 2013.
ENGLISH 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.
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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.

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We use simulated galaxy surveys to study: (i) how galaxy membership in redMaPPer clusters maps to the underlying halo population, and (ii) the accuracy of a mean dynamical cluster mass, M-sigma(lambda), derived from stacked pairwise spectroscopy of clusters with richness lambda. Using similar to 130 000 galaxy pairs patterned after the Sloan Digital Sky Survey (SDSS) redMaPPer cluster sample study of Rozo et al., we show that the pairwise velocity probability density function of central-satellite pairs with m(i) < 19 in the simulation matches the form seen in Rozo et al. Through joint membership matching, we deconstruct the main Gaussian velocity component into its halo contributions, finding that the top-ranked halo contributes similar to 60 per cent of the stacked signal. The halo mass scale inferred by applying the virial scaling of Evrard et al. to the velocity normalization matches, to within a few per cent, the log-mean halo mass derived through galaxy membership matching. We apply this approach, along with miscentring and galaxy velocity bias corrections, to estimate the log-mean matched halo mass at z = 0.2 of SDSS redMaPPer clusters. Employing the velocity bias constraints of Guo et al., we find aEuroln (M-200c)|lambda aEuro parts per thousand = ln (< M-30) + alpha(m) ln (lambda/30) with M-30 = 1.56 +/- 0.35 x 10(14) M-aS (TM) and alpha(m) = 1.31 +/- 0.06(stat) +/- 0.13(sys). Systematic uncertainty in the velocity bias of satellite galaxies overwhelmingly dominates the error budget.
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Dinsmore, Kimberly. "Factor and Cluster Analysis of Learning Orientation Questionnaire." Digital Commons @ East Tennessee State University, 2018. https://dc.etsu.edu/asrf/2018/schedule/103.

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The purpose was to evaluate the psychometric properties of a 25-item questionnaire on learning orientation in a nursing educational program. The questionnaire is a standardized instrument used in industry and universities but this is the first time its psychometric properties were assessed in a nursing students. Learning orientation is a vital aspect that includes elements such as: desire to learn, an outreaching toward the goal, and seriousness in using instructional resources provided. Four hundred and seventy-two undergraduate nursing students at the first semester junior level completed the Learning Orientation Questionnaire online. The data were imported into an R language processor (Version 3.3.1) for plotting and statistical analysis. This first step was manual extraction to identify correlations between items and group them into categories using cluster analysis. Six categories were formed: learning interest, ambitious goals, instructor, achievement of goals, and lone survivor. The second step was the automatic extraction of factors with functions in the R language. The items were grouped into four factors rather than six. The automatic extraction combined the factors learning interest and ambitious goals together and removed the lone survivor factor. The lone survivor factor contained one question (q24) with a low eigenvalue of (0.3). This question was deemed both minor and irrelevant to the topic of the questionnaire and therefore is proposed to be removed. The result of the factor and cluster analyses in nursing students was compared to that of a previous study (Martinez, 2005) that used a heterogeneous sample of both high school and university students of many different majors. The differences in the results were substantial. First, Martinez (2005) found three extracted factors in her research in comparison to the current research of four factors. Moreover, the previous study grouped our main two factors into a single factor, which means that the factors for learning interest, ambitious goals, and instructor influence were a single factor in the previous study. The eigenvalues were almost identical to the current research, yet the factors did not correspond. Another major difference between the two studies is that in the previous study’s second factor called “learning independence or autonomy” did not correlate with the description of the factor. The author explains the factor as growing to learn independently by finding the motivation from within. Yet, the questions selected in the factor solemnly incorporate the instructor as a resource of learning. In this study, the factor was placed in a separate category and was labeled “instructor influenced” based on the fact that it heavily relied on the demand of an instructor. The conclusions are: (1) The factors extracted in nursing students in the present study do not correspond closely to those of the sample used by Martinez (2005) and this might be explained by the greater focus on applied practice in nursing education. (2) The current questionnaire could be reduced in size to 8 questions while maintaining strong measurement quality.
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Stephens, Chad Louis. "Autonomic Differentiation of Emotions: A Cluster Analysis Approach." Thesis, Virginia Tech, 2007. http://hdl.handle.net/10919/79690.

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The autonomic specificity of emotion is intrinsic for many major theories of emotion. One of the goals of this study was to validate a standardized set of music clips to be used in studies of emotion and affect. This was accomplished using self-reported affective responses to 40 music pieces, noise, and silence clips in a sample of 71 college-aged individuals. Following the music selection phase of the study; the validated music clips as well as film clips previously shown to induce a wide array of emotional responses were presented to 50 college-aged subjects while a montage of autonomic variables were measured. Evidence for autonomic discrimination of emotion was found via pattern classification analysis replicating findings from previous research. It was theorized that groups of individuals could be identified based upon individual response specificity using cluster analytic techniques. Single cluster solutions for all emotion conditions indicated that stimulus response stereotypy of emotions was more powerful than individual patterns. Results from pattern classification analysis and cluster analysis support the concept of autonomic specificity of emotion.
Master of Science
[Appendix B: Beck Depression Inventory, p. 61-64, was removed Oct. 4, 2011 GMc]
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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.

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Καράγεωργα, Ισμήνη. "Ανάλυση συστάδων (cluster analysis)." Thesis, 2012. http://hdl.handle.net/10889/5932.

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Στη συγκεκριμένη διπλωματική εργασία αναλύεται το πρόβλημα της ανάλυσης συστάδων. Σκοπός της ανάλυσης συστάδων είναι να ομαδοποιεί τα στοιχεία σε cluster έτσι ώστε τα στοιχεία που ανήκουν στο ίδιο cluster να έχουν μεγαλύτερη ομοιότητα από τα στοιχεία που ανήκουν σε διαφορετικά cluster.
In 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.
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