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Dissertations / Theses on the topic 'K-means clustering algorithm'

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Kondo, Yumi. "Robustification of the sparse K-means clustering algorithm." Thesis, University of British Columbia, 2011. http://hdl.handle.net/2429/37093.

Li, Yanjun. "High Performance Text Document Clustering." Wright State University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=wright1181005422.

Xie, Qing Yan. "K-Centers Dynamic Clustering Algorithms and Applications." University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1384427644.

Zhao, Jianmin. "Optimal Clustering: Genetic Constrained K-Means and Linear Programming Algorithms." VCU Scholars Compass, 2006. http://hdl.handle.net/10156/1583.

Dineff, Dimitris. "Clustering using k-means algorithm in multivariate dependent models with factor structure." Thesis, Uppsala universitet, Tillämpad matematik och statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-429528.

Chahine, Firas Safwan. "A Genetic Algorithm that Exchanges Neighboring Centers for Fuzzy c-Means Clustering." NSUWorks, 2012. http://nsuworks.nova.edu/gscis_etd/116.

Bacak, Hikmet Ozge. "Decision Making System Algorithm On Menopause Data Set." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12612471/index.pdf.

Alsayat, Ahmed Mosa. "Efficient genetic k-means clustering algorithm and its application to data mining on different domains." Thesis, Bowie State University, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10239708.

Because of the massive increase for streams available and being produced, the areas of data mining and machine learning have become increasingly popular. This takes place as companies, organizations and industries seek out optimal methods and techniques for processing these large data sets. Machine learning is a branch of artificial intelligence that involves creating programs that autonomously perform different data mining techniques when exposed to data streams. The study evaluates at two very different domains in an effort to provide a better and more optimized applicable method of clustering than is currently being used. We examine the use of data mining in healthcare, as well as the use of these techniques in the social media domain. Testing the proposed technique on these two drastically different domains offers us valuable insights into the performance of the proposed technique across domains.

This study aims at reviewing the existing methods of clustering and presenting an enhanced k-means clustering algorithm by using a novel method called Optimize Cluster Distance (OCD) applied to social media domain. This (OCD) method maximizes the distance between clusters by pair-wise re-clustering to enhance the quality of the clusters. For the healthcare domain, the k-means was applied along with Self Organizing Map (SOM) to get an optimal number of clusters. The possibility of getting bad positions of centroids in k-means was solved by applying the Genetic algorithm to the k-means in social media and healthcare domains. The OCD was applied again to enhance the quality of the produced clusters. In both domains, compared to the conventional k-means, the analysis shows that the proposed k-means is accurate and achieves better clustering performance along with valuable insights for each cluster. The approach is unsupervised, scalable and can be applied to various domains.

Fu, Xuezheng. "Structure Pattern Analysis Using Term Rewriting and Clustering Algorithm." Digital Archive @ GSU, 2007. http://digitalarchive.gsu.edu/cs_diss/17.

Jung, Heejin. "A comparison of driving characteristics and environmental characteristics using factor analysis and k-means clustering algorithm." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/28778.

Mohd, Yusoff Sabariah. "Clustering classification and human perception of automative steering wheel transient vibrations." Thesis, Brunel University, 2017. http://bura.brunel.ac.uk/handle/2438/15849.

Drach, Tetjana Oleksandrivna, and Oleksandr Evgenovich Goloskokov. "Research and development of mathematical and software solutions of the information system of situational enterprise management." Thesis, NTU "KhPI", 2018. http://repository.kpi.kharkov.ua/handle/KhPI-Press/38079.

Zhong, Wei. "Clustering System and Clustering Support Vector Machine for Local Protein Structure Prediction." Digital Archive @ GSU, 2006. http://digitalarchive.gsu.edu/cs_diss/7.

Junior, Willian Darwin. "Agrupamento de textos utilizando divergência Kullback-Leibler." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/18/18153/tde-30032016-160011/.

Mao, Qian. "Clusters Identification: Asymmetrical Case." Thesis, Uppsala universitet, Informationssystem, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-208328.

Aksoy, Ece. "An Attempt To Classify Turkish District Data: K-means And Self-organizing Map (som) Algorithms." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12605711/index.pdf.

Nguyen, Thi Nguyet Que. "Nouveaux développements en histologie spectrale IR : application au tissu colique." Thesis, Reims, 2016. http://www.theses.fr/2016REIMS040/document.

Madjar, Nicole, and Filip Lindblom. "Machine Learning implementation for Stress-Detection." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280897.

Xiang, Chongyuan. "Private k-means clustering : algorithms and applications." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/106394.

Chowuraya, Tawanda. "Online content clustering using variant K-Means Algorithms." Thesis, Cape Peninsula University of Technology, 2019. http://hdl.handle.net/20.500.11838/3089.

Pettersson, Christoffer. "Investigating the Correlation Between Marketing Emails and Receivers Using Unsupervised Machine Learning on Limited Data : A comprehensive study using state of the art methods for text clustering and natural language processing." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-189147.

Salman, Raied. "CONTRIBUTIONS TO K-MEANS CLUSTERING AND REGRESSION VIA CLASSIFICATION ALGORITHMS." VCU Scholars Compass, 2012. http://scholarscompass.vcu.edu/etd/2738.

Jurásek, Petr. "Shlukování proteinových sekvencí na základě podobnosti primární struktury." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2009. http://www.nusl.cz/ntk/nusl-236761.

Sedláček, Josef. "Algoritmy pro shlukování textových dat." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2011. http://www.nusl.cz/ntk/nusl-218899.

Dsouza, Jeevan. "Region-based Crossover for Clustering Problems." NSUWorks, 2012. http://nsuworks.nova.edu/gscis_etd/139.

Jamborová, Soňa. "Segmentace obrazu pomocí neuronové sítě." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2011. http://www.nusl.cz/ntk/nusl-236925.

Abreu, Rodolfo Telo Martins de. "Algorithms for information extraction and signal annotation on long-term biosignals using clustering techniques." Master's thesis, Faculdade de Ciências e Tecnologia, 2012. http://hdl.handle.net/10362/8250.

Berglund, Jesper. "An automated approach to clustering with the framework suggested by Bradley, Fayyad and Reina." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-238736.

Xu, Beijie. "Clustering Educational Digital Library Usage Data: Comparisons of Latent Class Analysis and K-Means Algorithms." DigitalCommons@USU, 2011. https://digitalcommons.usu.edu/etd/954.

Pospíšil, David. "Shluková analýza signálu EKG." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2013. http://www.nusl.cz/ntk/nusl-219954.

Oliveira, Max Gontijo de. "Sistema de localização de facilidades: uma abordagem para mensuração de pontos de demanda e localização de facilidades." Universidade Federal de Goiás, 2012. http://repositorio.bc.ufg.br/tede/handle/tede/5512.

Durut, Matthieu. "Algorithmes de classification répartis sur le cloud." Phd thesis, Télécom ParisTech, 2012. http://tel.archives-ouvertes.fr/tel-00744768.

Curtin, Ryan Ross. "Improving dual-tree algorithms." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54354.

Mamani, Rodríguez Zoraida Emperatriz. "Aplicación de la minería de datos distribuida usando algoritmo de clustering k-means para mejorar la calidad de servicios de las organizaciones modernas caso: Poder judicial." Master's thesis, Universidad Nacional Mayor de San Marcos, 2015. https://hdl.handle.net/20.500.12672/4472.

Andrésen, Anton, and Adam Håkansson. "Comparing unsupervised clustering algorithms to locate uncommon user behavior in public travel data : A comparison between the K-Means and Gaussian Mixture Model algorithms." Thesis, Tekniska Högskolan, Jönköping University, JTH, Datateknik och informatik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-49243.

Schmidt, Melanie [Verfasser], Christian [Akademischer Betreuer] Sohler, and Johannes [Gutachter] Blömer. "Coresets and streaming algorithms for the k-means problem and related clustering objectives / Melanie Schmidt. Betreuer: Christian Sohler. Gutachter: Johannes Blömer." Dortmund : Universitätsbibliothek Dortmund, 2014. http://d-nb.info/1112267131/34.

Angeles, Bocanegra Oscar Raúl, and Quispe Cesar Abel Melgarejo. "Algoritmo de clustering utilizando k-means e índice de validación Rose turi para la segmentación de clientes de la Caja Rural Prymera." Bachelor's thesis, Universidad Nacional Mayor de San Marcos, 2012. https://hdl.handle.net/20.500.12672/12131.

Borges, Ederson. "Um novo algoritmo imunológico artificial para agrupamento de dados." Universidade Presbiteriana Mackenzie, 2010. http://tede.mackenzie.br/jspui/handle/tede/1511.

Wessman, Filip. "Advanced Algorithms for Classification and Anomaly Detection on Log File Data : Comparative study of different Machine Learning Approaches." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-43175.

Ren, Yi. "Indexation et recherche de similarités avec des descripteurs structurés par coupes d'images sur des graphes." Thesis, Bordeaux, 2014. http://www.theses.fr/2014BORD0215/document.

Wang, Kun. "Algorithmes et méthodes pour le diagnostic ex-situ et in-situ de systèmes piles à combustible haute température de type oxyde solide." Phd thesis, Université de Franche-Comté, 2012. http://tel.archives-ouvertes.fr/tel-01017170.

Šalplachta, Jakub. "Analýza 3D CT obrazových dat se zaměřením na detekci a klasifikaci specifických struktur tkání." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2017. http://www.nusl.cz/ntk/nusl-316836.

Hunter, Brandon. "Channel Probing for an Indoor Wireless Communications Channel." BYU ScholarsArchive, 2003. https://scholarsarchive.byu.edu/etd/64.

Lisák, Peter. "Rozpoznávání člověka podle žil prstu." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2011. http://www.nusl.cz/ntk/nusl-236991.

Rahmani, Hoda. "Traveling Salesman Problem with Single Truck and Multiple Drones for Delivery Purposes." Ohio University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1563894245160348.

Masetti, Masha. "Product Clustering e Machine Learning per il miglioramento dell'accuratezza della previsione della domanda: il caso Comer Industries S.p.A." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

Yang, Hong-Xiang, and 楊閎翔. "A Modified K-means Algorithm for Sequence Clustering." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/22490180184996174354.

Hsueh, Meng-Lun, and 薛孟倫. "Wheeze Detection using Modified k-Means Clustering Algorithm." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/25519734434912722746.

Lin, Jia-Zhi, and 林佳志. "Improving Clustering Efficiency by SimHash-based K-Means Algorithm." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/nv495x.

Chang, Tsun-Chuan, and 張宗荃. "Speeding up Fuzzy k-Means Clustering Algorithm on GPU." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/70355858766705138770.

Improved K-means clustering algorithms : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science, Massey University, New Zealand

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COMMENTS

  1. Dissertations / Theses: 'K-means clustering algorithm'

    The purpose of this thesis is to verify a modified K-means algorithm in asymmetrical cases, which can be regarded as an extension to the research of Vladislav Valkovsky and Mikael Karlsson in Department of Informatics and Media. In this thesis an experiment is designed and implemented to identify clusters with the modified algorithm in ...

  2. PDF k-means initialisation algorithms: an extensive comparative study

    The k-means data clustering algorithm, whilst widely popular, is not without its drawbacks. In this work, we are particularly interested in the sensitivity of k-means to its initialisation, in the form of a set of initial cen-troids. Since the cluster recovery performance of k-means can potentially be

  3. PDF Application of existing k-means algorithms for the evaluation of card

    The k-means algorithm is able to quickly and e ciently nd the homogeneous clusters from the given data. Researchers have modi ed the standard k-means algorithm to overcome its limitations, so that the better clustering results can be obtained. This thesis deals with the application of the standard k-means al-

  4. Private k-means clustering : algorithms and applications

    The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. ... This thesis describes the construction of small coresets for computing k-means clustering of a set of points while preserving differential privacy. As a result, it gives the first 푘-means clustering ...

  5. Improved K-means clustering algorithms : a thesis presented in partial

    K-means clustering algorithm is designed to divide the samples into subsets with the goal that maximizes the intra-subset similarity and inter-subset dissimilarity where the similarity measures the relationship between two samples. As an unsupervised learning technique, K-means clustering algorithm is considered one of the most used clustering algorithms and has been applied in a variety of ...

  6. PDF Determining k in k-means clustering by exploiting attribute distributions

    given. While the method proposed in this thesis could theoretically be applied with any clustering technique where k needs to be given, the focus will be on k-means clustering since it is a simple algo-rithm, but one of the most widely used (Berkhin, 2006). 1.1 K-Means The standard k-means algorithm, sometimes re-

  7. PDF Implementation of K-means Clustering for Value Portfolio Selection

    Keywords: cluster analysis, clustering, k-means, value investing, value anomaly, value stock, growth stock, valuation multiple This thesis examines the performance of value investing strategies in the U.S. stock market over the period 2003-2023 by implementing the k-means clustering algorithm for combining several value indicators.

  8. PDF A Generalization of K-Means Clustering Using Bregman Divergences

    K-Means algorithm that will be shown to converge. In addition, we establish a relationship between regular exponential families and Bregman divergences to develop an e cient EM scheme for learning mixtures of exponential family distributions that leads to a simple soft clustering algorithm. The primary source for this

  9. PDF Sindhuja Ranganathan Improvements to k-means clustering Master's Thesis

    k-means clustering algorithm particularly the improvements made by Hamerly [7] and Elkan [9]. The rest of this thesis is organized as follows: In Chapter 2 we review k-means clustering algorithm in general and its variants. Chapter 3 deals with the methods to optimizek-means. In particular we focus the work of Charles Elkan, Greg Hamerly. In

  10. PDF Using K-Means Clustering to Create Cost and Demand Functions that

    This thesis proposes a method to reduce the excess inventory and associated costs, while maintaining instant part availability, despite design changes which alter the number of parts required. A single period model extension was created based on K-means clustering of the parts according to lead-time and cost. These groupings provided the