Дисертації з теми "Cluster clustering"
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Dimitriadou, Evgenia, Andreas Weingessel, and Kurt Hornik. "A voting-merging clustering algorithm." SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, 1999. http://epub.wu.ac.at/94/1/document.pdf.
Повний текст джерелаSeries: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science"
Al-Razgan, Muna Saleh. "Weighted clustering ensembles." Fairfax, VA : George Mason University, 2008. http://hdl.handle.net/1920/3212.
Повний текст джерелаVita: 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.
Gaertler, Marco. "Clustering with spectral methods." [S.l. : s.n.], 2002. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB10101213.
Повний текст джерелаPtitsyn, Andrey. "New algorithms for EST clustering." Thesis, University of the Western Cape, 2000. http://etd.uwc.ac.za/index.php?module=etd&.
Повний текст джерелаKoepke, Hoyt Adam. "Bayesian cluster validation." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/1496.
Повний текст джерелаTittley, Eric Robert. "Hierarchical clustering and galaxy cluster scaling laws." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape9/PQDD_0008/NQ40291.pdf.
Повний текст джерелаKuah, Adrian T. H. "Determinants of clustering, cluster growth and performance." Thesis, University of Manchester, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.629921.
Повний текст джерелаShortreed, Susan. "Learning in spectral clustering /." Thesis, Connect to this title online; UW restricted, 2006. http://hdl.handle.net/1773/8977.
Повний текст джерелаChan, 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.
Повний текст джерелаMadureira, Erikson Manuel Geraldo Vieira de. "Análise de mercado : clustering." Master's thesis, Instituto Superior de Economia e Gestão, 2016. http://hdl.handle.net/10400.5/13122.
Повний текст джерелаO presente trabalho tem como objetivo descrever as atividades realizadas durante o estágio efetuado na empresa Quidgest. Tendo a empresa a necessidade de estudar as suas diversas vertentes de negócio, optou-se por extrair e identificar as informações presentes no banco de dados da empresa. Para isso, foi utilizado um processo conhecido na análise de dados denominado por Extração de Conhecimento em Bases de Dados (ECBD). O maior desafio na utilização deste processo deveu-se há grande acumulação de informação pela empresa, que se foi intensificando a partir de 2013. Das fases do processo de ECBD, a que tem maior relevância é o data mining, onde é feito um estudo das variáveis caracterizadoras necessárias para a análise em foco. Foi escolhida a técnica de análise cluster da fase de data mining para que que toda análise possa ser eficiente, eficaz e se possa obter resultados de fácil leitura. Após o desenvolvimento do processo de ECBD, foi decidido que a fase de data mining podia ser implementada de modo a facilitar um trabalho futuro de uma análise realizada pela empresa. Para implementar essa fase, utilizaram-se técnicas de análise cluster e foi desenvolvida um programa em VBA/Excel centrada no utilizador. Para testar o programa criado foi utilizado um caso concreto da empresa. Esse caso consistiu em determinar quais os atuais clientes que mais contribuíram para a evolução da empresa nos anos de 2013 a 2015. Aplicando o caso referido no programa criado, obtiveram-se resultados e informações que foram analisadas e interpretadas.
This paper aims to describe the activities performed during the internship made in Quidgest company. Having the company need to study their various business areas, it was decided to extract and identify the information contained in the company's database. For this end, we used a process known in the data analysis called for Knowledge Discovery in Databases (KDD). The biggest challenge in using this process was due to their large accumulation of information by the company, which was intensified from 2013. The phases of the KDD process, which is the most relevant is data mining, where a study of characterizing variables required for the analysis is done. The cluster analysis technique of data mining phase was chosen for that any analysis can be efficient, effective and could provide results easy to read. After the development of the KDD process, it was decided that the data mining phase could be automated to facilitate future work carried out by the company. To automate this phase, cluster analysis techniques were used and was developed a program in VBA/Excel user-centered. To test the created program we used a specific case of the company. This case consisted in determining the current customers that have contributed to the company's evolution during the years 2013-2015. The application of the program has revealed useful information that has been analyzed and interpreted.
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Gupta, Pramod. "Robust clustering algorithms." Thesis, Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/39553.
Повний текст джерелаZhang, Yiqun. "Advances in categorical data clustering." HKBU Institutional Repository, 2019. https://repository.hkbu.edu.hk/etd_oa/658.
Повний текст джерелаCole, Rowena Marie. "Clustering with genetic algorithms." University of Western Australia. Dept. of Computer Science, 1998. http://theses.library.uwa.edu.au/adt-WU2003.0008.
Повний текст джерелаLeisch, Friedrich. "Bagged clustering." SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, 1999. http://epub.wu.ac.at/1272/1/document.pdf.
Повний текст джерелаSeries: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science"
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.
Повний текст джерелаCui, Yingjie. "A study on privacy-preserving clustering." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B4357225X.
Повний текст джерелаKübler, Bernhard Christian. "Risk classification by means of clustering." Frankfurt, M. Berlin Bern Bruxelles New York, NY Oxford Wien Lang, 2009. http://d-nb.info/998737291/04.
Повний текст джерелаMcClelland, Robyn L. "Regression based variable clustering for data reduction /." Thesis, Connect to this title online; UW restricted, 2000. http://hdl.handle.net/1773/9611.
Повний текст джерелаLee, 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.
Повний текст джерелаSpeer, Nora. "Funktionelles Clustering von Genen mit der Gene Ontology /." Berlin : Logos-Verl, 2006. http://deposit.d-nb.de/cgi-bin/dokserv?id=2875270&prov=M&dok_var=1&dok_ext=htm.
Повний текст джерелаDimitriadou, Evgenia, Andreas Weingessel, and Kurt Hornik. "Fuzzy voting in clustering." SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, 1999. http://epub.wu.ac.at/742/1/document.pdf.
Повний текст джерелаSeries: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"
Zhou, Hong. "Visual clustering in parallel coordinates and graphs /." View abstract or full-text, 2009. http://library.ust.hk/cgi/db/thesis.pl?CSED%202009%20ZHOU.
Повний текст джерелаPourvali, Mohsen <1984>. "Improving the quality of text clustering and cluster labeling." Doctoral thesis, Università Ca' Foscari Venezia, 2016. http://hdl.handle.net/10579/10311.
Повний текст джерелаWang, 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.
Повний текст джерелаCui, Yingjie, and 崔英杰. "A study on privacy-preserving clustering." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B4357225X.
Повний текст джерелаXiong, Yimin. "Time series clustering using ARMA models /." View abstract or full-text, 2004. http://library.ust.hk/cgi/db/thesis.pl?COMP%202004%20XIONG.
Повний текст джерелаIncludes bibliographical references (leaves 49-55). Also available in electronic version. Access restricted to campus users.
Silva, Gustavo Girão Barreto da. "Resource-aware clustering design for NoC-based MPSoCs." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2014. http://hdl.handle.net/10183/95984.
Повний текст джерелаThe multicore paradigm is a solid trend nowadays, also in the field of embedded systems. The degree of parallelism provided by such architecture has been the foundation of performance advancements in the field as well as for power and energy savings. However, to obtain efficient parallelism of such architecture is not an easy task. Therefore, developers come up with several proposals of programming environments trying to provide as much transparency as possible. On the hardware side, this increasing number of on-chip components creates a management issue to be handled. In the context of this complex scenario this thesis proposes the use of resource management approaches to improve the efficiency, regarding both performance and energy consumption, of MPSoC environments at different levels. Also, these approaches have in common the notion of clustering, which tries to logically aggregate resources according to application demands. First, at the processor/application level, we propose a dynamically adaptable hardware to support distinct parallel programming models at no computational overhead, since the entire process is completely transparent to the programmer. Also, in this environment, where distinct applications can be executed, we propose a resource-aware scheduling mechanism to improve performance named Processor Clustering. We propose four different resource mapping policies that leverage on distinct aspects of the parallel nature of the applications and on architecture constraints. However, some applications have higher memory demands than computational demands. Therefore, a similar approach can be used at the memory level. In this case, we aim at redistributing memory resources according to application demands. We explore memory redistribution at both design time and runtime and propose a distribution mapping mechanism based on the amount of off-chip memory requests. Finally, we propose a resource-aware fault-tolerance mechanism for distributed on-chip memories in NoCs. We introduce a Reliability Clustering model that leverages on the NoC infrastructure. In this case, the routers have knowledge of faulty blocks and redundancy blocks and, based on that, they are able to avoid higher memory access latency.
Woo, Kam Tim. "Applications of clustering techniques on communication systems /." View abstract or full-text, 2004. http://library.ust.hk/cgi/db/thesis.pl?ELEC%202004%20WOO.
Повний текст джерелаButchart, Kate. "Hierarchical clustering using dynamic self organising neural networks." Thesis, University of Hertfordshire, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.338383.
Повний текст джерелаCorreia, 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.
Повний текст джерелаEsta 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
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.
Повний текст джерелаBigdeli, Elnaz. "Incremental Anomaly Detection Using Two-Layer Cluster-based Structure." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/34299.
Повний текст джерелаKumar, Swapnil. "Comparison of blocking and hierarchical ways to find cluster." Kansas State University, 2017. http://hdl.handle.net/2097/35425.
Повний текст джерелаDepartment of Computing and Information Sciences
William H. Hsu
Clustering in data mining is a process of discovering groups in a set of data such that the similarity within the group is maximized and the similarity among the groups is minimized. One way of approaching clustering is to treat it as a blocking problem of minimizing the maximum distance between any two units within the same group. This method is known as Threshold blocking. It works by applying blocking as a graph partition problem. Chameleon is a hierarchical clustering algorithm, that based on dynamic modelling measures the similarity between two clusters. In the clustering process, to merge two cluster, we check if the inter-connectivity and closeness between two clusters are high relative to the internal inter-connectivity of the clusters and closeness of items within the clusters. This way of merging of cluster using the dynamic model helps in discovery of natural and homogeneous clusters. The main goal of this project is to implement a local implementation of CHAMELEON and compare the output generated from Chameleon against Threshold blocking algorithm suggested by Higgins et al with its hybridized form and unhybridized form.
Wang, Xinyu. "Toward Scalable Hierarchical Clustering and Co-clustering Methods : application to the Cluster Hypothesis in Information Retrieval." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSE2123/document.
Повний текст джерелаAs a major type of unsupervised machine learning method, clustering has been widely applied in various tasks. Different clustering methods have different characteristics. Hierarchical clustering, for example, is capable to output a binary tree-like structure, which explicitly illustrates the interconnections among data instances. Co-clustering, on the other hand, generates co-clusters, each containing a subset of data instances and a subset of data attributes. Applying clustering on textual data enables to organize input documents and reveal connections among documents. This characteristic is helpful in many cases, for example, in cluster-based Information Retrieval tasks. As the size of available data increases, demand of computing power increases. In response to this demand, many distributed computing platforms are developed. These platforms use the collective computing powers of commodity machines to parallelize data, assign computing tasks and perform computation concurrently.In this thesis, we first address text clustering tasks by proposing two clustering methods, Sim_AHC and SHCoClust. They respectively represent a similarity-based hierarchical clustering and a similarity-based hierarchical co-clustering. We examine their properties and performances through mathematical deduction, experimental verification and evaluation. Then we apply these methods in testing the cluster hypothesis, which is the fundamental assumption in cluster-based Information Retrieval. In such tests, we apply the optimal cluster search to evaluation the retrieval effectiveness of different clustering methods. We examine the computing efficiency and compare the results of the proposed tests. In order to perform clustering on larger datasets, we select Apache Spark platform and provide distributed implementation of Sim_AHC and of SHCoClust. For distributed Sim_AHC, we present the designed computing procedure, illustrate confronted difficulties and provide possible solutions. And for SHCoClust, we provide a distributed implementation of its core, spectral embedding. In this implementation, we use several datasets that vary in size to examine scalability
Konda, Swetha Reddy. "Classification of software components based on clustering." Morgantown, W. Va. : [West Virginia University Libraries], 2007. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=5510.
Повний текст джерелаTitle from document title page. Document formatted into pages; contains vi, 59 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 57-59).
Zhang, Kai. "Kernel-based clustering and low rank approximation /." View abstract or full-text, 2008. http://library.ust.hk/cgi/db/thesis.pl?CSED%202008%20ZHANG.
Повний текст джерелаChan, 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.
Повний текст джерелаJohnston, Joshua Benjamin Hamerly Gregory James. "Clustering in high dimension and choosing cluster representatives for SimPoint." Waco, Tex. : Baylor University, 2007. http://hdl.handle.net/2104/5067.
Повний текст джерелаJia, Hong. "Clustering of categorical and numerical data without knowing cluster number." HKBU Institutional Repository, 2013. http://repository.hkbu.edu.hk/etd_ra/1495.
Повний текст джерелаDaumová, Dora. "Clustering as a Tool of Competitiveness, the Case of the Czech Republic." Master's thesis, Vysoká škola ekonomická v Praze, 2008. http://www.nusl.cz/ntk/nusl-4285.
Повний текст джерелаChang, Soong Uk. "Clustering with mixed variables /." [St. Lucia, Qld.], 2005. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe19086.pdf.
Повний текст джерелаRantes, García Mónica Tahiz, and Quispe Lizbeth María Cruz. "Detección de fraudes usando técnicas de clustering." Universidad Nacional Mayor de San Marcos. Programa Cybertesis PERÚ, 2010. http://www.cybertesis.edu.pe/sisbib/2010/rantes_gm/html/index-frames.html.
Повний текст джерелаThe credit card fraud is one of the most important problems currently facing financial institutions. While technology has enhanced security in credit cards with the use of PINs, the introduction of chips on the cards, the use of additional keys as tokens and improvements in the regulation of their use, is also a need for banks, act preemptively against this crime. To act proactively need real-time monitoring operations are carried out and have the ability to react promptly against any questionable transaction that takes place. Clustering technique tackle this problem is a common method since it allows the grouping of data allowing classifying them by their similarity according to some metric, this measure of similarity is based on the attributes that describe the objects. Moreover, this technique is very sensitive to Outlier tool that is characterized by the impact they cause on the statistic when going to analyze the data
Xiong, Huojin. "Clustering in the Field of Vocational Education." Doctoral thesis, Universitätsbibliothek Chemnitz, 2013. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-113319.
Повний текст джерелаThis dissertation applies comparative methods to make analyses on some selected implementation modes of clustering in the field of vocational education in China. Based on the structural, hierarchical and functional aspects of the theory of system, and also in consideration of the social economical and educational features of clustering in the field of vocational education, Porter’s theory and its amended models, theory of human capital and theory of education are reviewed for the choice of comparative criteria. On the basis of the available information, some representative implementation models are selected from Henan (province, South China), Shanghai (provincial level city, East China), Hainan (province, Central China), Yongchuan (prefectural level city, West China) and Yantai (Prefectural level city, North China). All the experiences from these areas are grouped and compared in two categories according to their features: professional clustering and regional clustering. And comparative analyses are made in reference to the above-mentioned three criteria. In consideration of the problems revealed in the implementation models, some international experiences are referred as examples in some practical aspects, such as of how to connect factors for clustering, of how to assist the clustering to live through its whole lifespan, and of how to get enterprises involved. Furthermore, some suggestions for future development of clustering are also made from theoretical point of view
Cruz, Quispe Lizbeth María, and García Mónica Tahiz Rantes. "Detección de fraudes usando técnicas de clustering." Bachelor's thesis, Universidad Nacional Mayor de San Marcos, 2010. https://hdl.handle.net/20.500.12672/2644.
Повний текст джерела---The credit card fraud is one of the most important problems currently facing financial institutions. While technology has enhanced security in credit cards with the use of PINs, the introduction of chips on the cards, the use of additional keys as tokens and improvements in the regulation of their use, is also a need for banks, act preemptively against this crime. To act proactively need real-time monitoring operations are carried out and have the ability to react promptly against any questionable transaction that takes place. Clustering technique tackle this problem is a common method since it allows the grouping of data allowing classifying them by their similarity according to some metric, this measure of similarity is based on the attributes that describe the objects. Moreover, this technique is very sensitive to Outlier tool that is characterized by the impact they cause on the statistic when going to analyze the data.
Tesis
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.
Повний текст джерелаEldridge, Justin Eldridge. "Clustering Consistently." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1512070374903249.
Повний текст джерела梁德貞 and Tak-ching Leung. "Correspondence analysis and clustering with applications to site-species occurrence." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1991. http://hub.hku.hk/bib/B31209889.
Повний текст джерелаLeung, Tak-ching. "Correspondence analysis and clustering with applications to site-species occurrence /." [Hong Kong] : University of Hong Kong, 1991. http://sunzi.lib.hku.hk/hkuto/record.jsp?B13039519.
Повний текст джерелаNdebele, Nothando Elizabeth. "Clustering algorithms and their effect on edge preservation in image compression." Thesis, Rhodes University, 2009. http://hdl.handle.net/10962/d1008210.
Повний текст джерелаSong, Liumeng. "Cluster heads selection and cooperative nodes selection for cluster-based Internet of Things networks." Thesis, Queen Mary, University of London, 2017. http://qmro.qmul.ac.uk/xmlui/handle/123456789/24778.
Повний текст джерела