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Dissertations / Theses on the topic 'Clustering of text information'

Author: Grafiati

Published: 28 May 2022

Last updated: 30 July 2025

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1

Rosell, Magnus. "Text Clustering Exploration : Swedish Text Representation and Clustering Results Unraveled." Doctoral thesis, KTH, Numerisk Analys och Datalogi, NADA, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-10129.

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Text clustering divides a set of texts into clusters (parts), so that texts within each cluster are similar in content. It may be used to uncover the structure and content of unknown text sets as well as to give new perspectives on familiar ones. The main contributions of this thesis are an investigation of text representation for Swedish and some extensions of the work on how to use text clustering as an exploration tool. We have also done some work on synonyms and evaluation of clustering results. Text clustering, at least such as it is treated here, is performed using the vector space model

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2

Viana, Hugo Henrique Amorim. "Automatic information retrieval through text-mining." Master's thesis, Faculdade de Ciências e Tecnologia, 2013. http://hdl.handle.net/10362/11308.

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The dissertation presented for obtaining the Master’s Degree in Electrical Engineering and Computer Science, at Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia<br>Nowadays, around a huge amount of firms in the European Union catalogued as Small and Medium Enterprises (SMEs), employ almost a great portion of the active workforce in Europe. Nonetheless, SMEs cannot afford implementing neither methods nor tools to systematically adapt innovation as a part of their business process. Innovation is the engine to be competitive in the globalized environment, especially in the current

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3

Wang, Wei. "Unsupervised Information Extraction From Text - Extraction and Clustering of Relations between Entities." Phd thesis, Université Paris Sud - Paris XI, 2013. http://tel.archives-ouvertes.fr/tel-00998390.

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Unsupervised information extraction in open domain gains more and more importance recently by loosening the constraints on the strict definition of the extracted information and allowing to design more open information extraction systems. In this new domain of unsupervised information extraction, this thesis focuses on the tasks of extraction and clustering of relations between entities at a large scale. The objective of relation extraction is to discover unknown relations from texts. A relation prototype is first defined, with which candidates of relation instances are initially extracted wit

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Pasi, Niharika. "Analysis of online news media through visualisation and text clustering." Thesis, Uppsala universitet, Informationssystem, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-361562.

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Online news has grown in frequency and popularity as a convenient source of information for several years. A result of this drastic surge is the increased competition for viewer-ship and prolonged relevance of online news websites. Higher demands by internet audiences have led to the use of sensationalism such as ‘clickbait’ articles or ‘fake news’ to attract more viewers. The subsequent shift in the journalistic approach in new media opened new opportunities to study the behaviour and intent behind the news content. As news publications cater their news to a specific target audience, conclusi

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5

Calli, Cagatay. "Improving Search Result Clustering By Integrating Semantic Information From Wikipedia." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612554/index.pdf.

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Suffix Tree Clustering (STC) is a search result clustering (SRC) algorithm focused on generating overlapping clusters with meaningful labels in linear time. It showed the feasibility of SRC but in time, subsequent studies introduced description-first algorithms that generate better labels and achieve higher precision. Still, STC remained as the fastest SRC algorithm and there appeared studies concerned with different problems of STC. In this thesis, semantic relations between cluster labels and documents are exploited to filter out noisy labels and improve merging phase of STC. Wikipedia is us

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6

Katerattanakul, Nitsawan. "A pilot study in an application of text mining to learning system evaluation." Diss., Rolla, Mo. : Missouri University of Science and Technology, 2010. http://scholarsmine.mst.edu/thesis/pdf/Katerattanakul_09007dcc807b614f.pdf.

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Thesis (M.S.)--Missouri University of Science and Technology, 2010.<br>Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed June 19, 2010) Includes bibliographical references (p. 72-75).

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7

Pourvali, Mohsen <1984&gt. "Improving the quality of text clustering and cluster labeling." Doctoral thesis, Università Ca' Foscari Venezia, 2016. http://hdl.handle.net/10579/10311.

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The abundance of available electronic information is rapidly increasing with the advancements in digital processing. Furthermore, huge amounts of textual data have given rise to the need for efficient techniques that can organize the data in manageable forms. In order to tackle this challenge, clustering algorithms try to group automatically similar documents. While clustering plays a significant role that helps to categorize documents, it owes intrinsic limits when it comes to allowing human users to understand the content of documents at a deeper level. This is where cluster labeling techniq

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8

Hou, Jun. "Text mining with semantic annotation : using enriched text representation for entity-oriented retrieval, semantic relation identification and text clustering." Thesis, Queensland University of Technology, 2014. https://eprints.qut.edu.au/79206/1/Jun_Hou_Thesis.pdf.

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This project is a step forward in the study of text mining where enhanced text representation with semantic information plays a significant role. It develops effective methods of entity-oriented retrieval, semantic relation identification and text clustering utilizing semantically annotated data. These methods are based on enriched text representation generated by introducing semantic information extracted from Wikipedia into the input text data. The proposed methods are evaluated against several start-of-art benchmarking methods on real-life data-sets. In particular, this thesis improves the

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9

Zhu, Weizhong Allen Robert B. "Text clustering and active learning using a LSI subspace signature model and query expansion /." Philadelphia, Pa. : Drexel University, 2009. http://hdl.handle.net/1860/3077.

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10

Pratiffi, Michele. "Tecniche di text mining per l'autoorganizzazione della conoscenza." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2013. http://amslaurea.unibo.it/6092/.

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L'elaborato ha come scopo l'analisi delle tecniche di Text Mining e la loro applicazione all'interno di processi per l'auto-organizzazione della conoscenza. La prima parte della tesi si concentra sul concetto del Text Mining. Viene fornita la sua definizione, i possibili campi di utilizzo, il processo di sviluppo che lo riguarda e vengono esposte le diverse tecniche di Text Mining. Si analizzano poi alcuni tools per il Text Mining e infine vengono presentati alcuni esempi pratici di utilizzo. Il macro-argomento che viene esposto successivamente riguarda TuCSoN, una infrastruttura per la coordi

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Groth, Philip. "Knowledge management and discovery for genotype/phenotype data." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, 2009. http://dx.doi.org/10.18452/16033.

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Die Untersuchung des Phänotyps bringt z.B. bei genetischen Krankheiten ein Verständnis der zugrunde liegenden Mechanismen mit sich. Aufgrund dessen wurden neue Technologien wie RNA-Interferenz (RNAi) entwickelt, die Genfunktionen entschlüsseln und mehr phänotypische Daten erzeugen. Interpretation der Ergebnisse solcher Versuche ist insbesondere bei heterogenen Daten eine große Herausforderung. Wenige Ansätze haben bisher Daten über die direkte Verknüpfung von Genotyp und Phänotyp hinaus interpretiert. Diese Dissertation zeigt neue Methoden, die Entdeckungen in Phänotypen über Spezies und Meth

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Alise, Dario Fioravante. "Algoritmo di "Label Propagation" per il clustering di documenti testuali." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14388/.

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Negli ultimi anni del secolo scorso l’avvento di Internet ha permesso di avere a disposizione innumerevoli quantità di testi consultabili online, provenienti sia da libri e riviste, sia da nuove forme di comunicazione della rete quali email, forum, newsgroup e chat.  Le soluzioni adottate nel settore del Text Mining (d’ora in poi abbreviato in TM), che è l’estensione del Data Mining rivolto a dati testuali non strutturati, si basano su fondamenti informatici, statistici e linguistici e sono in linea di principio applicabili a documenti di qualsiasi dimensione. Con l’avvento dei Social Networks

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Сидорова, М. Г., та І. С. Коробіхіна. "Математичне та програмне забезпечення кластеризації текстової інформації". Thesis, Сумський державний університет, 2017. http://essuir.sumdu.edu.ua/handle/123456789/66863.

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Fratesi, Sarah Elizabeth. "The Virtual Landscape of Geological Information Topics, Methods, and Rhetoric in Modern Geology." [Tampa, Fla] : University of South Florida, 2008. http://purl.fcla.edu/usf/dc/et/SFE0002777.

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15

Dail, Mathias. "Clustering unstructured life sciences experiments with unsupervised machine learning : Natural language processing for unstructured life sciences texts." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-265549.

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The purpose of this master’s thesis is to analyse different types of document representations in the context of improving, in an unsupervised manner, the searchability of unstructured textual life sciences experiments by clustering similar experiments together. The challenge is to produce, analyse and compare different representations of the life sciences data by using traditional and advanced unsupervised Machine learning models. The text data analysed in this work is noisy and very heterogeneous, as it comes from a real-world Electronic Lab Notebook. Clustering unstructured and unlabeled tex

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16

Marcacini, Ricardo Marcondes. "Aprendizado de máquina com informação privilegiada: abordagens para agrupamento hierárquico de textos." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-05082015-094733/.

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Métodos de agrupamento hierárquico de textos são muito úteis para analisar o conhecimento embutido em coleções textuais, organizando os documentos textuais em grupos e subgrupos para facilitar a exploração do conhecimento em diversos níveis de granularidade. Tais métodos pertencem à área de aprendizado não supervisionado de máquina, uma que vez obtêm modelos de agrupamento apenas pela observação de regularidades existentes na coleção textual, sem supervisão humana. Os métodos tradicionais de agrupamento assumem que a coleção textual é representada apenas pela informação técnica, ou seja, palav

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Prokopp, Christian Werner. "Semantic service discovery in the service ecosystem." Thesis, Queensland University of Technology, 2011. https://eprints.qut.edu.au/50872/1/Christian_Prokopp_Thesis.pdf.

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Electronic services are a leitmotif in ‘hot’ topics like Software as a Service, Service Oriented Architecture (SOA), Service oriented Computing, Cloud Computing, application markets and smart devices. We propose to consider these in what has been termed the Service Ecosystem (SES). The SES encompasses all levels of electronic services and their interaction, with human consumption and initiation on its periphery in much the same way the ‘Web’ describes a plethora of technologies that eventuate to connect information and expose it to humans. Presently, the SES is heterogeneous, fragmented and co

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18

Leixner, Petr. "Shlukování textových dat." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2010. http://www.nusl.cz/ntk/nusl-237188.

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Process of text data clustering can be used to analysis, navigation and structure large sets of texts or hypertext documents. The basic idea is to group the documents into a set of clusters on the basis of their similarity. The well-known methods of text clustering, however, do not really solve the specific problems of text clustering like high dimensionality of the input data, very large size of the databases and understandability of the cluster description. This work deals with mentioned problems and describes the modern method of text data clustering based on the use of frequent term sets,

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19

Bobrik, Annette Verfasser], and Hermann [Akademischer Betreuer] [Krallmann. "Content-based Clustering in Social Corpora - A New Method for Knowledge Identification based on Text Mining and Cluster Analysis / Annette Bobrik. Betreuer: Hermann Krallmann." Berlin : Universitätsbibliothek der Technischen Universität Berlin, 2013. http://d-nb.info/1031075364/34.

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Bobrik, Annette [Verfasser], and Hermann [Akademischer Betreuer] Krallmann. "Content-based Clustering in Social Corpora - A New Method for Knowledge Identification based on Text Mining and Cluster Analysis / Annette Bobrik. Betreuer: Hermann Krallmann." Berlin : Universitätsbibliothek der Technischen Universität Berlin, 2013. http://nbn-resolving.de/urn:nbn:de:kobv:83-opus-38461.

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21

Kini, Ananth Ullal. "On the effect of INQUERY term-weighting scheme on query-sensitive similarity measures." Texas A&M University, 2005. http://hdl.handle.net/1969.1/3116.

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Cluster-based information retrieval systems often use a similarity measure to compute the association among text documents. In this thesis, we focus on a class of similarity measures named Query-Sensitive Similarity (QSS) measures. Recent studies have shown QSS measures to positively influence the outcome of a clustering procedure. These studies have used QSS measures in conjunction with the ltc term-weighting scheme. Several term-weighting schemes have superseded the ltc term-weighing scheme and demonstrated better retrieval performance relative to the latter. We test whether introducing one

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Narreddy, Naga Sambu Reddy, and Tuğrul Durgun. "Clusters (k) Identification without Triangle Inequality : A newly modelled theory." Thesis, Uppsala universitet, Institutionen för informatik och media, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-183608.

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Cluster analysis characterizes data that are similar enough and useful into meaningful groups (clusters).For example, cluster analysis can be applicable to find group of genes and proteins that are similar, to retrieve information from World Wide Web, and to identify locations that are prone to earthquakes. So the study of clustering has become very important in several fields, which includes psychology and other social sciences, biology, statistics, pattern recognition, information retrieval, machine learning and data mining [1] [2]. Cluster analysis is the one of the widely used technique

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23

Pereira, Andre Luiz Vizine. "Busca na web e agrupamento de textos usando computação inspirada na biologia." [s.n.], 2007. http://repositorio.unicamp.br/jspui/handle/REPOSIP/259821.

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Orientadores: Ricardo Ribeiro Gudwin, Leandro Nunes de Castro Silva<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação<br>Made available in DSpace on 2018-08-11T06:40:01Z (GMT). No. of bitstreams: 1 Pereira_AndreLuizVizine_M.pdf: 1817378 bytes, checksum: 1d28283d8d2855800dd0f406eb97e5e0 (MD5) Previous issue date: 2007<br>Resumo: A Internet tornou-se um dos principais meios de comunicação da atualidade, reduzindo custos, disponibilizando recursos e informação para pessoas das mais diversas áreas e interesses. Esta dissertação desenv

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Wives, Leandro Krug. "Um estudo sobre agrupamento de documentos textuais em processamento de informações não estruturadas usando técnicas de "clustering"." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 1999. http://hdl.handle.net/10183/25933.

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Atualmente, técnicas de recuperação e análise de informações, principalmente textuais, são de extrema importância. Após o grande BOOM da Internet, muitos problemas que já eram conhecidos em contextos fechados passaram a preocupar também toda a comunidade científica. No âmbito deste trabalho os problemas relacionados à sobrecarga de informações, que ocorre devido ao grande volume de dados a disposição de uma pessoa, são os mais importantes. Visando minimizar estes problemas, este trabalho apresenta um estudo sobre métodos de agrupamento de objetos textuais (documentos no formato ASCII), onde os

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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/.

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O presente trabalho propõe uma metodologia para agrupamento de textos que possa ser utilizada tanto em busca textual em geral como mais especificamente na distribuição de processos jurídicos para fins de redução do tempo de resolução de conflitos judiciais. A metodologia proposta utiliza a divergência Kullback-Leibler aplicada às distribuições de frequência dos radicais (semantemas) das palavras presentes nos textos. Diversos grupos de radicais são considerados, formados a partir da frequência com que ocorrem entre os textos, e as distribuições são tomadas em relação a cada um desses grupos. P

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Kästel, Arne Morten, and Christian Vestergaard. "Comparing performance of K-Means and DBSCAN on customer support queries." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-260252.

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In customer support, there are often a lot of repeat questions, and questions that does not need novel answers. In a quest to increase the productivity in the question answering task within any business, there is an apparent room for automatic answering to take on some of the workload of customer support functions. We look at clustering corpora of older queries and texts as a method for identifying groups of semantically similar questions and texts that would allow a system to identify new queries that fit a specific cluster to receive a connected, automatic response. The approach compares the

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Tagny, Ngompe Gildas. "Méthodes D'Analyse Sémantique De Corpus De Décisions Jurisprudentielles." Thesis, IMT Mines Alès, 2020. http://www.theses.fr/2020EMAL0002.

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Une jurisprudence est un corpus de décisions judiciaires représentant la manière dont sont interprétées les lois pour résoudre un contentieux. Elle est indispensable pour les juristes qui l'analysent pour comprendre et anticiper la prise de décision des juges. Son analyse exhaustive est difficile manuellement du fait de son immense volume et de la nature non-structurée des documents. L'estimation du risque judiciaire par des particuliers est ainsi impossible car ils sont en outre confrontés à la complexité du système et du langage judiciaire. L'automatisation de l'analyse des décisions permet

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NUNES, IAN MONTEIRO. "CLUSTERING TEXT STRUCTURED DATA BASED ON TEXT SIMILARITY." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2008. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=25796@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>PROGRAMA DE EXCELENCIA ACADEMICA<br>O presente trabalho apresenta os resultados que obtivemos com a aplicação de grande número de modelos e algoritmos em um determinado conjunto de experimentos de agrupamento de texto. O objetivo de tais testes é determinar quais são as melhores abordagens para processar as grandes massas de informação geradas pelas crescentes demandas de data quality em diversos setores da economia. O processo de deduplicação foi acelerado pela divisão dos con

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

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Yadav, Jyoti. "Fuzzer Test Log Analysis Using Machine Learning : Framework to analyze logs and provide feedback to guide the fuzzer." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254893.

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In this modern world machine learning and deep learning have become popular choice for analysis and identifying various patterns on data in large volumes. The focus of the thesis work has been on the design of the alternative strategies using machine learning to guide the fuzzer in selecting the most promising test cases. Thesis work mainly focuses on the analysis of the data by using machine learning techniques. A detailed analysis study and work is carried out in multiple phases. First phase is targeted to convert the data into suitable format(pre-processing) so that necessary features can

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Liliemark, Adam, and Viktor Enghed. "Categorization of Customer Reviews Using Natural Language Processing." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-299882.

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Databases of user generated data can quickly become unmanageable. Klarna faced this issue, with a database of around 700,000 customer reviews. Ideally, the database would be cleaned of uninteresting reviews and the remaining reviews categorized. Without knowing what categories might emerge, the idea was to use an unsupervised clustering algorithm to ﬁnd categories. This thesis describes the work carried out to solve this problem, and proposes a solution for Klarna that involves artiﬁcial neural networks rather than unsupervised clustering. The implementation done by us is able to categorize re

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Karatzoglou, Alexandros, and Ingo Feinerer. "Text Clustering with String Kernels in R." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 2006. http://epub.wu.ac.at/1002/1/document.pdf.

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We present a package which provides a general framework, including tools and algorithms, for text mining in R using the S4 class system. Using this package and the kernlab R package we explore the use of kernel methods for clustering (e.g., kernel k-means and spectral clustering) on a set of text documents, using string kernels. We compare these methods to a more traditional clustering technique like k-means on a bag of word representation of the text and evaluate the viability of kernel-based methods as a text clustering technique. (author's abstract)<br>Series: Research Report Series / Depar

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Annerwall, Staffan. "Exploring Short Text Clustering for Transactional Data." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447520.

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The digital revolution has led to an increase in digitization of transactional information. Due to the large amount of data, the transactions must be categorized such that an overview of spending can be obtained. To aid the process of manually classifying transactions, we consider a process of clustering short text transactional data as a pre-processing step. If clusters have high homogeneity, then entire clusters, and hence multiple transactions, can be classified at once. We explore two short text clustering methods, and evaluate them on real-world data in terms of execution time and cluster

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Jing, Liping. "Text subspace clustering with feature weighting and ontologies." Click to view the E-thesis via HKUTO, 2007. http://sunzi.lib.hku.hk/hkuto/record/B39332834.

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Jing, Liping, and 景麗萍. "Text subspace clustering with feature weighting and ontologies." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B39332834.

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Gränsbo, Gustav. "Word Clustering in an Interactive Text Analysis Tool." Thesis, Linköpings universitet, Interaktiva och kognitiva system, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157497.

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A central operation of users of the text analysis tool Gavagai Explorer is to look through a list of words and arrange them in groups. This thesis explores the use of word clustering to automatically arrange the words in groups intended to help users. A new word clustering algorithm is introduced, which attempts to produce word clusters tailored to be small enough for a user to quickly grasp the common theme of the words. The proposed algorithm computes similarities among words using word embeddings, and clusters them using hierarchical graph clustering. Multiple variants of the algorithm are

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Diesner, Jana. "Uncovering and Managing the Impact of Methodological Choices for the Computational Construction of Socio-Technical Networks from Texts." Research Showcase @ CMU, 2012. http://repository.cmu.edu/dissertations/194.

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This thesis is motivated by the need for scalable and reliable methods and technologies that support the construction of network data based on information from text data. Ultimately, the resulting data can be used for answering substantive and graph-theoretical questions about socio-technical networks. One main limitation with constructing network data from text data is that the validation of the resulting network data can be hard to infeasible, e.g. in the cases of covert, historical and large-scale networks. This thesis addresses this problem by identifying the impact of coding choices that

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Buchta, Christian, Martin Kober, Ingo Feinerer, and Kurt Hornik. "Spherical k-Means Clustering." American Statistical Association, 2012. http://epub.wu.ac.at/4000/1/paper.pdf.

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Clustering text documents is a fundamental task in modern data analysis, requiring approaches which perform well both in terms of solution quality and computational efficiency. Spherical k-means clustering is one approach to address both issues, employing cosine dissimilarities to perform prototype-based partitioning of term weight representations of the documents. This paper presents the theory underlying the standard spherical k-means problem and suitable extensions, and introduces the R extension package skmeans which provides a computational environment for spherical k-means clusteri

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Hossain, Mahmud Shahriar. "Apriori approach to graph-based clustering of text documents." Thesis, Montana State University, 2008. http://etd.lib.montana.edu/etd/2008/hossain/HossainM0508.pdf.

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This thesis report introduces a new technique of document clustering based on frequent senses. The developed system, named GDClust (Graph-Based Document Clustering) [1], works with frequent senses rather than dealing with frequent keywords used in traditional text mining techniques. GDClust presents text documents as hierarchical document-graphs and uses an Apriori paradigm to find the frequent subgraphs, which reflect frequent senses. Discovered frequent subgraphs are then utilized to generate accurate sense-based document clusters. We propose a novel multilevel Gaussian minimum support strat

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Houé, Maxime. "Clustering of short sentences through representation of text data." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-266121.

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Natural Language Processing has developed in the past few years very quickly.Numerous new applications emerged from new methods, notably involved bythe creation of the popular word embedding Word2Vec created by a team ofGoogle researchers. One of these new applications is the chatbot technology.The goal of these conversational interfaces is to be able to communicate automaticallywith humans via written or voice chat. With a chatbot, a companyhopes to improve customer relations at a lower cost. Unfortunately, the skills ofthe chatbots can vary a lot, and until now, their understanding of the hu

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41

Kural, S. Yasemin. "Clustering information retrieval search outputs." Thesis, City University London, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.312900.

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Jo, Taeho. "The implementation of dynamic document organization using the integration of text clustering and text categorization." Thesis, University of Ottawa (Canada), 2006. http://hdl.handle.net/10393/29353.

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A document organization is a collection of documents composed of labeled clusters that contain similar documents. In any information system, a collection of documents always changes as time goes, since users access the collection to delete, add, and update documents. Dynamic Document Organization is a document organization that adapts automatically to such variable document collections. DDO poses two challenges, because of the decentralized mode of access. First, some clusters may have many documents, while others may have very few. Second, documents belonging to new topics may be added to the

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Ranby, Erik. "A comparison of clustering techniques for short social text messages." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-196735.

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The amount of social text messages authored each day is huge and the information contained within is potentially very valuable. Software that can cluster and thereby help analyze these messages would consequently be helpful. This thesis explores several ways of clustering social text messages. Two algorithms and several setups with these algorithms have been tested and evaluated with the same data as input. Based on these evaluations, a comparison has been conducted in order to answer the question which algorithm setup is best suited for the task. The two clustering algorithms that have been t

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Barnard, Sydney A. "Content-based Clustering and Visualization of Social Media Text Messages." Wright State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=wright1527085083542772.

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Crossman, Nathaniel C. "Stream Clustering And Visualization Of Geotagged Text Data For Crisis Management." Wright State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=wright1590957641168863.

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Alqadah, Faris. "Clustering of Multi-Domain Information Networks." University of Cincinnati / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1275657962.

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Law, Hiu Chung. "Clustering, dimensionality reduction, and side information." Diss., Connect to online resource - MSU authorized users, 2006.

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Thesis (Ph. D.)--Michigan State University. Dept. of Computer Science & Engineering, 2006.<br>Title from PDF t.p. (viewed on June 19, 2009) Includes bibliographical references (p. 296-317). Also issued in print.

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Richards, Eric D. "Goal information and text comprehension." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape10/PQDD_0003/MQ41764.pdf.

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Civera, Saiz Jorge. "An evaluation of alternative strategies for clustering genes from medical text." Thesis, Georgia Institute of Technology, 2003. http://hdl.handle.net/1853/8664.

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Moreno, José G. "Text-Based Ephemeral Clustering for Web Image Retrieval on Mobile Devices." Caen, 2014. http://www.theses.fr/2014CAEN2036.

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Dans cette thèse, nous présentons une étude sur la visualisation des résultats Web d'images sur les dispositifs nomades. Nos principales conclusions ont été inspirées par les avancées récentes dans deux principaux domaines de recherche – la recherche d'information et le traitement automatique du langage naturel. Tout d’abord, nous avons examiné différents sujets tels que le regroupement des résultats Web, les interfaces mobiles, la fouille des intentions sur une requête, pour n'en nommer que quelques-uns. Ensuite, nous nous sommes concentré sur les mesures d'association lexical, les métriques

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