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Eslava-Gomez, Guillermina. "Projection pursuit and other graphical methods for multivariate data." Thesis, University of Oxford, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.236118.
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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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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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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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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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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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Jarvis, Paul S. "Determining geographical causal relationships through the development of spatial cluster detection and feature selection techniques." Thesis, University of South Wales, 2006. https://pure.southwales.ac.uk/en/studentthesis/determining-geographical-casual-relationships-through-the-development-of-spatial-cluster-detection-and-feature-selection-techniques(7a882804-5565-44d7-8635-e59c66e2e9bc).html.
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Spatial datasets contain information relating to the locations of incidents of a disease or other phenomena. Appropriate analysis of such datasets can reveal information about the distribution of cases of the phenomena. Areas that contain higher than expected incidence of the phenomena, given the background population, are of particular interest. Such clusters of cases may be affected by external factors. By analysing the locations of potential influences, it may be possible to establish whether a cause and effect relationship is present within the dataset. This thesis describes research that has led to the development and application of cluster detection and feature selection techniques in order to determine whether causal relationships are present within generic spatial datasets. The techniques are described and demonstrated, and their effectiveness established by testing them using synthetic datasets. The techniques are then applied to a dataset supplied by the Welsh Leukaemia Registry that details all cases of leukaemia diagnosed in Wales between 1990 and 2000. Cluster detection techniques can be used to provide information about case distribution. A novel technique, CLAP, has been developed that scans the study region and identifies the statistical significance of the levels of incidence in specific areas. Feature selection techniques can be used to identify the extent to which a selection of inputs impact upon a given output. Results from CLAP are combined with details of the locations of potential causal factors, in the form of a numerical dataset that can be analysed using feature selection techniques. Established techniques and a newly developed technique are used for the analysis. Results from such analysis allow conclusions to be drawn as to whether geographical causal relationships are apparent.
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Takahashi, Atsushi. "Hierarchical Cluster Analysis of Dense GNSS Data and Interpretation of Cluster Characteristics." Kyoto University, 2019. http://hdl.handle.net/2433/244510.
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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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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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Li, Hao. "Feature cluster selection for high-dimensional data analysis." Diss., Online access via UMI:, 2007.
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Hobbs, Mike. "Genetic algorithms for spatial data analysis in geographical information systems." Thesis, University of Kent, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.262636.
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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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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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Bolton, Richard John. "Multivariate analysis of multiproduct market research data." Thesis, University of Exeter, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.302542.
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VALE, MARCOS NEVES DO. "DATA CLUSTERING: ANALYSIS OF METHODS AND DEVELOPMENT OF APPLICATION FOR CLUSTER ANALYSIS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2005. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=7975@1.
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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIROA enorme massa de dados que é gerada pelas diversas empresas diariamente pode conter informações importantes que não são fáceis de serem extraídas. Com isso advém a necessidade de analisá-los automaticamente, de forma adequada, extraindo informação útil que pode agregar algum tipo de conhecimento. Uma das formas de se analisar os dados automaticamente é através da análise de agrupamentos. Ela procura encontrar grupos de dados semelhantes entre si. As técnicas de análise de agrupamentos revelam como os dados estão estruturados e resultam em um melhor entendimento sobre o negócio. Existe ainda hoje uma escassez de ferramentas para esse fim. Em um problema real de agrupamento de dados convém analisar os dados através da utilização de diferentes métodos, a fim de buscar aquele que melhor se adapte ao problema. Porém, as ferramentas existentes hoje em dia não são integradas, onde cada ferramenta possui um subconjunto dos métodos existentes de agrupamento. Dessa forma o usuário fica limitado à utilização de uma ferramenta específica ou é obrigado a conhecer diversas ferramentas diferentes, de forma a melhor analisar os dados de sua empresa. Esta dissertação apresenta uma revisão detalhada de todo o processo de análise de agrupamentos e o desenvolvimento de um aplicativo que visa não apenas a atender as deficiências presentes na maioria das ferramentas com esse fim, mas também a auxiliar, de forma mais completa, todo o processo de análise dos grupos. O aplicativo desenvolvido é de fácil utilização e permite que a ele sejam incorporados outros métodos eventualmente desenvolvidos pelo usuário. O aplicativo foi avaliado em três estudos de casos, os quais visam demonstrar a facilidade de uso do aplicativo, assim como avaliar as vantagens do uso de métodos de natureza fuzzy em uma base de dados real.
The enormous data mass that is daily generated by several companies can contain critical information that might not be easily retrieved, considering that the amount of data is generally huge and/or the target information might be spread through different data bases. Taking that into consideration, it might be necessary to properly analyze the data in an automatic way, so useful and valuable information can be extracted. One way of automatically analyzing data is through cluster analysis. This type of analysis searches for related similar data. These clusters settle a data structure model and with proper analysis can reveal important information. The techniques used in cluster analysis disclose how data is structured and allow a better knowledge of the business. Still today there is a lack of tools for this purpose. On a real situation with a data cluster problem it is wise to analyze the data through different methods, so we can find the one that better fits the problem. However, today the existing tools are not integrated, and each tool has a subgroup of existing cluster methods. This way the user stays limited to use only one specific tool or is forced to be aware of a number of different tools, so he would be able to better analyze the company data. This study presents a detailed review of the whole group analysis process and develops an application that not only suggests how to cover the currently lack of tools for this purpose, but also to help the complete cluster analysis process in a more extended way. The application developed is user friendly and allows other methods developed by users to be incorporated. The application has been evaluated into three case studies with the purpose of demonstrating its user friendly, as well as evaluating the advantages of using fuzzy methods on a true data base.
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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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McClelland, Robyn L. "Regression based variable clustering for data reduction /." Thesis, Connect to this title online; UW restricted, 2000. http://hdl.handle.net/1773/9611.
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Tavares, Nuno Filipe Ramalho da Cunha. "Multivariate analysis applied to clinical analysis data." Master's thesis, Faculdade de Ciências e Tecnologia, 2014. http://hdl.handle.net/10362/12288.
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Dissertação para obtenção do Grau de Mestre em Engenharia e Gestão IndustrialFolate, vitamin B12, iron and hemoglobin are essential for metabolic functions in the body. The deficiency of these can be the cause of several known pathologies and, untreated, can be responsible for severe morbidity and even death. The objective of this study is to characterize a population, residing in the metropolitan area of Lisbon and Setubal, concerning serum levels of folate, vitamin B12, iron and hemoglobin, as well as finding evidence of correlations between these parameters and illnesses, mainly cardiovascular, gastrointestinal, neurological and anemia. Clinical analysis data was collected and submitted to multivariate analysis. First the data was screened with Spearman correlation and Kruskal-Wallis analysis of variance to study correlations and variability between groups. To characterize the population, we used cluster analysis with Ward’s linkage method. Finally a sensitivity analysis was performed to strengthen the results. A positive correlation between iron with, ferritin and transferrin, and with hemoglobin was observed with the Spearman correlation. Kruskal-Wallis analysis of variance test showed significant differences between these biomarkers in persons aged 0 to 29, 30 to 59 and over 60 years old. Cluster analysis proved to be a useful tool when characterizing a population based on its biomarkers, showing evidence of low folate levels for the population in general, and hemoglobin levels below the reference values. Iron and vitamin B12 were within the reference range for most of the population. Low levels of the parameters were registered mainly in patients with cardiovascular, gastrointestinal, and neurological diseases and anemia.
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Zhang, Yiqun. "Advances in categorical data clustering." HKBU Institutional Repository, 2019. https://repository.hkbu.edu.hk/etd_oa/658.
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Categorical data are common in various research areas, and clustering is a prevalent technique used for analyse them. However, two challenging problems are encountered in categorical data clustering analysis. The first is that most categorical data distance metrics were actually proposed for nominal data (i.e., a categorical data set that comprises only nominal attributes), ignoring the fact that ordinal attributes are also common in various categorical data sets. As a result, these nominal data distance metrics cannot account for the order information of ordinal attributes and may thus inappropriately measure the distances for ordinal data (i.e., a categorical data set that comprises only ordinal attributes) and mixed categorical data (i.e., a categorical data set that comprises both ordinal and nominal attributes). The second problem is that most hierarchical clustering approaches were actually designed for numerical data and have very high computation costs; that is, with time complexity O(N2) for a data set with N data objects. These issues have presented huge obstacles to the clustering analysis of categorical data. To address the ordinal data distance measurement problem, we studied the characteristics of ordered possible values (also called 'categories' interchangeably in this thesis) of ordinal attributes and propose a novel ordinal data distance metric, which we call the Entropy-Based Distance Metric (EBDM), to quantify the distances between ordinal categories. The EBDM adopts cumulative entropy as a measure to indicate the amount of information in the ordinal categories and simulates the thinking process of changing one's mind between two ordered choices to quantify the distances according to the amount of information in the ordinal categories. The order relationship and the statistical information of the ordinal categories are both considered by the EBDM for more appropriate distance measurement. Experimental results illustrate the superiority of the proposed EBDM in ordinal data clustering. In addition to designing an ordinal data distance metric, we further propose a unified categorical data distance metric that is suitable for distance measurement of all three types of categorical data (i.e., ordinal data, nominal data, and mixed categorical data). The extended version uniformly defines distances and attribute weights for both ordinal and nominal attributes, by which the distances measured for the two types of attributes of a mixed categorical data can be directly combined to obtain the overall distances between data objects with no information loss. Extensive experiments on all three types of categorical data sets demonstrate the effectiveness of the unified distance metric in clustering analysis of categorical data. To address the hierarchical clustering problem of large-scale categorical data, we propose a fast hierarchical clustering framework called the Growing Multi-layer Topology Training (GMTT). The most significant merit of this framework is its ability to reduce the time complexity of most existing hierarchical clustering frameworks (i.e., O(N2)) to O(N1.5) without sacrificing the quality (i.e., clustering accuracy and hierarchical details) of the constructed hierarchy. According to our design, the GMTT framework is applicable to categorical data clustering simply by adopting a categorical data distance metric. To make the GMTT framework suitable for the processing of streaming categorical data, we also provide an incremental version of GMTT that can dynamically adopt new inputs into the hierarchy via local updating. Theoretical analysis proves that the GMTT frameworks have time complexity O(N1.5). Extensive experiments show the efficacy of the GMTT frameworks and demonstrate that they achieve more competitive categorical data clustering performance by adopting the proposed unified distance metric.
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Nunn, Martha E. "Influence diagnostics for correlated data /." Thesis, Connect to this title online; UW restricted, 1997. http://hdl.handle.net/1773/9590.
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Spanopoulos, Georgios. "Density Enhancements in the Solar Wind Plasma - Cluster Data Analysis." Thesis, KTH, Rymd- och plasmafysik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-121330.
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In this study density variations in the solar wind are examined based on data from the Cluster Mission. The data are originating from the stream outside the bowshock and thus they are spanning in an interval of three to four months for each mission year up to 2006. As the data are examined, variations above the relative electron density threshold of 1.3 are archived. The variations are analyzed in terms of position, orientation, magnetic field perturbation and scale sizes. The magnetic field perturbations are exhibiting diamagnetic and paramagnetic behavior and a possible link to similar observations inside the magnetosphere is attempted through the impulsive penetration mechanism. The final conclusion of the report is that plasma density enhancements, similar to those identified from previous studies inside the magnetosphere, are also evident in the free solar wind stream close to earth.
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Hammah, Reginald Edmund. "Intelligent delineation of rock discontinuity data using fuzzy cluster analysis." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape11/PQDD_0012/NQ41436.pdf.
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Cunningham, Gordon John. "Application of cluster analysis to high-throughput multiple data types." Thesis, University of Glasgow, 2011. http://theses.gla.ac.uk/2715/.
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PolySNAP is a program used for analysis of high-throughput powder diffraction data. The program matches diffraction patterns using Pearson and Spearman correlation coefficients to measure the similarity of the profiles of each pattern with every other pattern, which creates a correlation matrix. This correlation matrix is then used to partition the patterns into groups using a variety of cluster analysis methods. The original version could not handle any data types other than powder X-ray Diffraction. The aim of this project was to expand the methods used in PolySNAP to allow it to analyse other data types, in particular Raman spectroscopy, differential scanning calorimetry and infrared spectroscopy data. This involves the preparation of suitable compounds which can be analysed using these techniques. The main compounds studied are sulfathiazole, carbamazepine and piroxicam. Some additional studies have been carried out on other datasets, including a test on an unseen dataset to test the efficacy of the methods. The optimal method for clustering any unknown dataset has also been determined.
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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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Robson, Geoffrey. "Multiple outlier detection and cluster analysis of multivariate normal data." Thesis, Stellenbosch : Stellenbosch University, 2003. http://hdl.handle.net/10019.1/53508.
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Thesis (MscEng)--Stellenbosch University, 2003.ENGLISH ABSTRACT: Outliers may be defined as observations that are sufficiently aberrant to arouse the suspicion of the analyst as to their origin. They could be the result of human error, in which case they should be corrected, but they may also be an interesting exception, and this would deserve further investigation. Identification of outliers typically consists of an informal inspection of a plot of the data, but this is unreliable for dimensions greater than two. A formal procedure for detecting outliers allows for consistency when classifying observations. It also enables one to automate the detection of outliers by using computers. The special case of univariate data is treated separately to introduce essential concepts, and also because it may well be of interest in its own right. We then consider techniques used for detecting multiple outliers in a multivariate normal sample, and go on to explain how these may be generalized to include cluster analysis. Multivariate outlier detection is based on the Minimum Covariance Determinant (MCD) subset, and is therefore treated in detail. Exact bivariate algorithms were refined and implemented, and the solutions were used to establish the performance of the commonly used heuristic, Fast–MCD.
AFRIKAANSE OPSOMMING: Uitskieters word gedefinieer as waarnemings wat tot s´o ’n mate afwyk van die verwagte gedrag dat die analis wantrouig is oor die oorsprong daarvan. Hierdie waarnemings mag die resultaat wees van menslike foute, in welke geval dit reggestel moet word. Dit mag egter ook ’n interressante verskynsel wees wat verdere ondersoek benodig. Die identifikasie van uitskieters word tipies informeel deur inspeksie vanaf ’n grafiese voorstelling van die data uitgevoer, maar hierdie benadering is onbetroubaar vir dimensies groter as twee. ’n Formele prosedure vir die bepaling van uitskieters sal meer konsekwente klassifisering van steekproefdata tot gevolg hˆe. Dit gee ook geleentheid vir effektiewe rekenaar implementering van die tegnieke. Aanvanklik word die spesiale geval van eenveranderlike data behandel om noodsaaklike begrippe bekend te stel, maar ook aangesien dit in eie reg ’n area van groot belang is. Verder word tegnieke vir die identifikasie van verskeie uitskieters in meerveranderlike, normaal verspreide data beskou. Daar word ook ondersoek hoe hierdie idees veralgemeen kan word om tros analise in te sluit. Die sogenaamde Minimum Covariance Determinant (MCD) subversameling is fundamenteel vir die identifikasie van meerveranderlike uitskieters, en word daarom in detail ondersoek. Deterministiese tweeveranderlike algoritmes is verfyn en ge¨ımplementeer, en gebruik om die effektiwiteit van die algemeen gebruikte heuristiese algoritme, Fast–MCD, te ondersoek.
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Kapp, Amy Virginia. "Cluster analysis of microarray data using the in-group proportion /." May be available electronically:, 2007. http://proquest.umi.com/login?COPT=REJTPTU1MTUmSU5UPTAmVkVSPTI=&clientId=12498.
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Collin, Sara, and Ingrid Möllerberg. "Designing an Interactive tool for Cluster Analysis of Clickstream Data." Thesis, Uppsala universitet, Avdelningen för visuell information och interaktion, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-414237.
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The purpose of this study was to develop an interactive tool that enables identification of different types of users of an application based on clickstream data. A complex hierarchical clustering algorithm tool called Recursive Hierarchical Clustering (RHC) was used. RHC provides a visualisation of user types as clusters, where each cluster has its own distinguishing action pattern, i.e., one or several consecutive actions made by the user in the application. A case study was conducted on the mobile application Plick, which is an application for selling and buying second hand clothes. During the course of the project, the analysis and its result was discovered to be difficult to understand by the operators of the tool. The interactive tool had to be extended to visualise the complex analysis and its result in an intuitive way. A literature study of how humans interpret information, and how to present it to operators, was conducted and led to a redesign of the tool. More information was added to each cluster to enable further understanding of the clustering results. A clustering reconfiguration option was also created where operators of the tool got the possibility to interact with the analysis. In the reconfiguration, the operator could change the input file of the cluster analysis and thus the end result. Usability tests showed that the extra added information about the clusters served as an amplification and a verification of the original results presented by RHC. In some cases the original result presented by RHC was used as a verification to user group identification made by the operator solely based on the extra added information. The usability tests showed that the complex analysis with its results could be understood and configured without considerable comprehension of the algorithm. Instead it seemed like it could be successfully used in order to identify user types with help of visual clues in the interface and default settings in the reconfiguration. The visualisation tool is shown to be successful in identifying and visualising user groups in an intuitive way.
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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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Hou, Jun. "Using Hadoop to Cluster Data in Energy System." University of Dayton / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1430092547.
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Jia, Hong. "Clustering of categorical and numerical data without knowing cluster number." HKBU Institutional Repository, 2013. http://repository.hkbu.edu.hk/etd_ra/1495.
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Postigo, Smura Michel Alexander. "Cluster analysis on sparse customer data on purchase of insurance products." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-249558.
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This thesis work aims at performing a cluster analysis on customer data of insurance products. Three different clustering algorithms are investigated. These are K-means (center-based clustering), Two-Level clustering (SOM and Hierarchical clustering) and HDBSCAN (density-based clustering). The input to the algorithms is a high-dimensional and sparse data set. It contains information about the customers previous purchases, how many of a product they have bought and how much they have paid. The data set is partitioned in four different subsets done with domain knowledge and also preprocessed by normalizing respectively scaling before running the three different cluster algorithms on it. A parameter search is performed for each of the cluster algorithms and the best clustering is compared with the other results. The best is measured by the highest average silhouette index. The results indicates that all of the three algorithms performs approximately equally good, with single exceptions. However, it can be stated that the algorithm showing best general results is K-means on scaled data sets. The different preprocessings and partitions of the data impacts the results in different ways and this shows that it is important to preprocess the input data in several ways when performing a cluster analysis.Målet med detta examensarbete är att utföra en klusteranalys på kunddata av försäkringsprodukter. Tre olika klusteralgoritmer undersöks. Dessa är Kmeans (center-based clustering), Two-Level clustering (SOM och Hierarchical clustering) och HDBSCAN (density-based clustering). Input till algoritmerna är ett högdimensionellt och glest dataset. Det innhåller information om kundernas tidigare köp, hur många produkter de har köpt och hur mycket de har betalat. Datasetet delas upp i fyra delmängder med kunskap inom området och förarbetas också genom att normaliseras respektive skalas innan klustringsalgoritmerna körs på det. En parametersökning utförs för dem tre olika algoritmerna och den bästa klustringen jämförs med de andra resultaten. Den bästa algoritmen bestäms genom att beräkna the högsta silhouette index-medelvärdet. Resultaten indikerar att alla tre algoritmerna levererar ungefärligt lika bra resultat, med enstaka undantag. Dock, kan det bekräftas att algoritmen som visar bäst resultat överlag är K-means på skalade dataset. De olika förberedelserna och uppdelningarna av datasetet påverkar resultaten på olika sätt och detta tyder på vikten av att förbereda input datat på flera sätt när en klusteranalys utförs.
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Lee, King-for Foris. "Clustering uncertain data using Voronoi diagram." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B43224131.
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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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Ma, Jingsheng. "Integrating GIS and spatial statistical tools for the spatial analysis of health-related data." Thesis, University of Sheffield, 2001. http://etheses.whiterose.ac.uk/14818/.
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Spatial Statistical Analysis (SSA) and Geographical Information Systems (GIS) are instrumental in many areas of geographical study. However, their use tends to be separate one from another. This has prevented their potential in many application areas from being realised. This research is an attempt to bring the two technologies together for a specific application area - health research. There are two research objectives. The first and main objective is to construct a software package - SAGE - by integrating necessary SSA techniques with ARC/INFO, a GIS, to enable the user to undertake a coherent study of area-based health-related data. The second objective is to evaluate and demonstrate SAGE through a case study. A range of SSA techniques was identified to be useful for addressing typical health questions. A three-tier client-server model was suggested and argued to be the most appropriate for integration as it takes advantages of both the loose-coupling and close-coupling approaches. Under this model, a SSA component forms the client, while ARCH/INFO functions as the server. They are linked through the middle tier - the linking agent. The development of SAGE provided experiences useful for developing a generic SSA module in the future for any GIS that confonns to a set of well-defined standard application interfaces. An empirical study of colorectal cancer (CRC) incidence for the city of Sheffield using SAGE is presented. It shows the usefulness of the SAGE regionalisation tool in constructing an appropriate regional framework for subsequent data analyses and of both exploratory and confirmatory spatial data analysis in exploring the characteristics of CRC incidence. Some weaknesses of SAGE are identified, while remedies for them are suggested. Future work is recommended. The SAGE User Guide, related publications and the SAGE source and executable code as well as the data used in the case study are enclosed for reference.
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Sivertun, Åke. "Geographical Information Systems (GIS) as a tool for analysis and communications of multidimensional data." Doctoral thesis, Umeå universitet, Institutionen för geografi och ekonomisk historia, 1993. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-100703.
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An integrating approach, including knowledge about whole systems of processes, is essential in order to reach both development and environmental protection goals. In this thesis Geographical Information Systems (GIS) are suggested as a tool to realise such integrated models. The main hypothesis in this work is that several natural technical and social systems that share a time-space can be compared and analysed in a GIS. My first objective was to analyze how GIS can support research, planning, and, more specifically, bring a broad scattering of competence together in an interdisciplinary process. In this process GIS was ivestigated as a tool to achieve models that give us a better overview of a problem, a better understanding for the processes involved, aid in foreseeing conflicts between interests, find ecological limits and assist in choosing countermeasures and monitor the result of different programs. The second objective concerns the requirement that models should be comparable and possible to include in other models and that they can be communicated to planners, politicians and the public. For this reason the possibilities to communicate the result and model components of multidimensional and multi-temporal data are investigated. Four examples on the possibilities and problems when using GIS in interdisciplinary studies are presented. In the examples, water plays a central role as a component in questions about development, management and environmental impact. The first articles focus on non-point source pollutants as a problem under growing attention when the big industrial and municipal point sources are brought under control. To manage non-point source pollutants, detailed knowledge about local conditions is required to facilitate precise advices on land use. To estimate the flow of metals and N(itrogen) in an area it is important to identify the soil moisture. Soil moisture changes over time but also significantly in the landscape according to several factors. Here a method is presented that calculate soil moisture over large areas. Man as a hydrologie factor has to be assessed to also understand the relative importance of anthropogen processes. To offer a supplement to direct measurements and add anthropogen factors, a GIS model is presented that takes soil-type, topography, vegetation, land-use, agricultural drainage and relative position in the watershed into account. A method to analyse and visualise development over time and space in the same model is presented in the last empirical study. The development of agricultural drainage can be discussed as a product of several forces here analyzed together and visualized with help of colour coded "Hyper pixels" and maps. Finally a discussion concerning the physiological and psychological possibilities to communicate multidimensional phenomena with the help of pictures and maps is held. The main conclusions in this theses are that GIS offer the possibilities to develop distributed models, e.g., models that calculate effects from a vide range of factors in larger areas and with a much higher spatial resolution than has been possible earlier. GIS also offer a possibility to integrate and communicate information from different disciplines to scientists, decision makers and the public.Diss. (sammanfattning) Umeå : Umeå universitet, 1993, härtill 6 uppsatser.
digitalisering@umu
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Harrington, Justin. "Extending linear grouping analysis and robust estimators for very large data sets." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/845.
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Cluster analysis is the study of how to partition data into homogeneous subsets so that the partitioned data share some common characteristic. In one to three dimensions, the human eye can distinguish well between clusters of data if clearly separated. However, when there are more than three dimensions and/or the data is not clearly separated, an algorithm is required which needs a metric of similarity that quantitatively measures the characteristic of interest.
Linear Grouping Analysis (LGA, Van Aelst et al. 2006) is an algorithm for clustering data around hyperplanes, and is most appropriate when: 1) the variables are related/correlated, which results in clusters with an approximately linear structure; and
2) it is not natural to assume that one variable is a “response”, and the remainder the “explanatories”.
LGA measures the compactness within each cluster via the sum of squared orthogonal distances to hyperplanes formed from the data.
In this dissertation, we extend the scope of problems to which LGA can be applied. The first extension relates to the linearity requirement inherent within LGA, and proposes a new method of non-linearly transforming the data into a Feature Space, using the Kernel Trick, such that in this space the data might then form linear clusters. A possible side effect of this transformation is that the dimension of the transformed space is significantly larger than the number of observations in a given cluster, which causes problems with orthogonal regression. Therefore, we also introduce a new method for calculating the distance of an observation to a cluster when its covariance matrix is rank deficient.
The second extension concerns the combinatorial problem for optimizing a LGA objective function, and adapts an existing algorithm, called BIRCH, for use in providing fast, approximate solutions, particularly for the case when data does not fit in memory. We also provide solutions based on BIRCH for two other challenging optimization problems in the field of robust statistics, and demonstrate, via simulation study as well as application on actual data sets, that the BIRCH solution compares favourably to the existing state-of-the-art alternatives, and in many cases finds a more optimal solution.
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Bergström, Martin. "Feature extraction and cluster analysis of oil slicks using optical satellite data." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-181682.
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Ramirez, Jon. "Analysis of compute cluster nodes with varying memory hierarchy distributions." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2009. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.
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Lee, King-for Foris, and 李敬科. "Clustering uncertain data using Voronoi diagram." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B43224131.
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Chan, Yat-ling, and 陳逸靈. "An optimization algorithm for clustering using weighted dissimilarity measures." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B26667009.
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Lai, Yiu-ming, and 黎耀明. "Automatic identification of hot topics and user clusters from online discussion forums." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B47849952.
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With the advancement of Internet technology and the changes in the mode
of communications, it is found that much first-hand news have been discussed
in Internet forums well before they are reported in traditional mass media.
Also, this communication channel provides an effective channel for illegal activities
such as dissemination of copyrighted movies, threatening messages and
online gambling etc. The law enforcement agencies are looking for solutions to
monitor these discussion forums for possible criminal activities and download
suspected postings as evidence for investigation. The volume of postings is
huge, for 10 popular forums in Hong Kong; we found that there are 300,000
new messages every day. In this thesis, we propose an automatic system that
tackles this problem. Our proposed system downloads postings from selected
discussion forums continuously and employs data mining techniques to identify
hot topics and cluster authors into different groups using word based user
profiles. Using these data, we try to locate some useful trends and detect crime
from the data, the result is discussed afterward with include advantages and
limitations of different approaches and at the end, there is a conclusion of the
way to solve those problems and provide future direction of this research.published_or_final_version
Computer Science
Master
Master of Philosophy
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Al-Razgan, Muna Saleh. "Weighted clustering ensembles." Fairfax, VA : George Mason University, 2008. http://hdl.handle.net/1920/3212.
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Thesis (Ph.D.)--George Mason University, 2008.Vita: p. 134. Thesis director: Carlotta Domeniconi. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Information Technology. Title from PDF t.p. (viewed Oct. 14, 2008). Includes bibliographical references (p. 128-133). Also issued in print.
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Fiero, Mallorie H. "Statistical Approaches for Handling Missing Data in Cluster Randomized Trials." Diss., The University of Arizona, 2016. http://hdl.handle.net/10150/612860.
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In cluster randomized trials (CRTs), groups of participants are randomized as opposed to individual participants. This design is often chosen to minimize treatment arm contamination or to enhance compliance among participants. In CRTs, we cannot assume independence among individuals within the same cluster because of their similarity, which leads to decreased statistical power compared to individually randomized trials. The intracluster correlation coefficient (ICC) is crucial in the design and analysis of CRTs, and measures the proportion of total variance due to clustering. Missing data is a common problem in CRTs and should be accommodated with appropriate statistical techniques because they can compromise the advantages created by randomization and are a potential source of bias. In three papers, I investigate statistical approaches for handling missing data in CRTs. In the first paper, I carry out a systematic review evaluating current practice of handling missing data in CRTs. The results show high rates of missing data in the majority of CRTs, yet handling of missing data remains suboptimal. Fourteen (16%) of the 86 reviewed trials reported carrying out a sensitivity analysis for missing data. Despite suggestions to weaken the missing data assumption from the primary analysis, only five of the trials weakened the assumption. None of the trials reported using missing not at random (MNAR) models. Due to the low proportion of CRTs reporting an appropriate sensitivity analysis for missing data, the second paper aims to facilitate performing a sensitivity analysis for missing data in CRTs by extending the pattern mixture approach for missing clustered data under the MNAR assumption. I implement multilevel multiple imputation (MI) in order to account for the hierarchical structure found in CRTs, and multiply imputed values by a sensitivity parameter, k, to examine parameters of interest under different missing data assumptions. The simulation results show that estimates of parameters of interest in CRTs can vary widely under different missing data assumptions. A high proportion of missing data can occur among CRTs because missing data can be found at the individual level as well as the cluster level. In the third paper, I use a simulation study to compare missing data strategies to handle missing cluster level covariates, including the linear mixed effects model, single imputation, single level MI ignoring clustering, MI incorporating clusters as fixed effects, and MI at the cluster level using aggregated data. The results show that when the ICC is small (ICC ≤ 0.1) and the proportion of missing data is low (≤ 25\%), the mixed model generates unbiased estimates of regression coefficients and ICC. When the ICC is higher (ICC > 0.1), MI at the cluster level using aggregated data performs well for missing cluster level covariates, though caution should be taken if the percentage of missing data is high.
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Ptitsyn, Andrey. "New algorithms for EST clustering." Thesis, University of the Western Cape, 2000. http://etd.uwc.ac.za/index.php?module=etd&.
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Expressed sequence tag database is a rich and fast growing source of data for gene expression analysis and drug discovery. Clustering of raw EST data is a necessary step for further analysis and one of the most challenging problems of modem computational biology.
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Georgi, Benjamin [Verfasser]. "Context-specific independence mixture models for cluster analysis of biological data / Benjamin Georgi." Berlin : Freie Universität Berlin, 2009. http://d-nb.info/102366402X/34.
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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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Li, Junjie. "Some algorithmic studies in high-dimensional categorical data clustering and selection number of clusters." HKBU Institutional Repository, 2008. http://repository.hkbu.edu.hk/etd_ra/1011.
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French, Benjamin. "Analysis of aggregate longitudinal data with time-dependent exposure /." Thesis, Connect to this title online; UW restricted, 2008. http://hdl.handle.net/1773/9569.
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Ranade, Sonia. "The application of cluster analysis to predicting the cellular uptake of foreign compounds." Thesis, University of Sheffield, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.284389.
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