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Dissertations / Theses on the topic 'Data Subgroup'

Author: Grafiati
Published: 14 March 2023
Last updated: 31 July 2025

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1

Atzmüller, Martin. "Knowledge-intensive subgroup mining : techniques for automatic and interactive discovery /." Berlin : Aka, 2007. http://deposit.d-nb.de/cgi-bin/dokserv?id=2928288&prov=M&dok_var=1&dok_ext=htm.

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2

Atzmüller, Martin. "Knowledge-intensive subgroup mining techniques for automatic and interactive discovery." Berlin Aka, 2006. http://deposit.d-nb.de/cgi-bin/dokserv?id=2928288&prov=M&dok_var=1&dok_ext=htm.

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3

Belfodil, Aimene. "An order theoretic point-of-view on subgroup discovery." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEI078.

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Comme le titre pourrait le suggérer, l’objectif principal de cette thèse est de fournir une meilleure compréhension de la tâche de la découverte de sous-groupes à travers la théorie de l’ordre. La découverte de sous-groupes (Subgroup Discovery - SD) est la tâche automatique dont le but est la découverte d’hypothèses intéressantes dans les bases de données. Autrement dit, étant donnée une base de donnée, l’espace de recherche de toutes les hypothèses que l’analyste voudra tester ainsi qu’un moyen formel pour évaluer la qualité de ces hypothèses ; la tâche automatique de la découverte de sous-gr
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4

Queyrel, Maxence. "End-to-End Deep Learning and Subgroup discovery approaches to learn from metagenomics data." Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS470.

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Les progrès technologiques ont rendu possible le séquençage haute résolution du matériel génétique à faible coût. Dans ce contexte, le microbiome humain a démontré sa grande capacité à stratifier diverses maladies humaines. En tant que "super-intégrateur" de l'état du patient, le microbiote intestinal est appelé à jouer un rôle clé dans la médecine de précision. L'identification de biomarqueurs omiques est devenue un objectif majeur en métagénomique, et permet de comprendre les diversités microbiennes qui induisent la stratification des patients. Il reste de nombreux défis associés aux pipelin
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5

Mistry, Dipesh. "Recursive partitioning based approaches for low back pain subgroup identification in individual patient data meta-analyses." Thesis, University of Warwick, 2014. http://wrap.warwick.ac.uk/64032/.

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This thesis presents two novel approaches for performing subgroup analyses or identifying subgroups in an individual patient data (IPD) meta-analyses setting. The work contained in this thesis originated from an important research priority in the area of low back pain (LBP); identifying subgroups that most (or least) benefit from treatment. Typically, a subgroup is evaluated by applying a statistical test for interaction between a baseline characteristic and treatment. A systematic review found that subgroup analyses in the area of LBP are severely underpowered and are of a rather poor quality
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6

Doubleday, Kevin. "Generation of Individualized Treatment Decision Tree Algorithm with Application to Randomized Control Trials and Electronic Medical Record Data." Thesis, The University of Arizona, 2016. http://hdl.handle.net/10150/613559.

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With new treatments and novel technology available, personalized medicine has become a key topic in the new era of healthcare. Traditional statistical methods for personalized medicine and subgroup identification primarily focus on single treatment or two arm randomized control trials (RCTs). With restricted inclusion and exclusion criteria, data from RCTs may not reflect real world treatment effectiveness. However, electronic medical records (EMR) offers an alternative venue. In this paper, we propose a general framework to identify individualized treatment rule (ITR), which connects the subg
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7

Mueller, Marianne Larissa [Verfasser], Stefan [Akademischer Betreuer] Kramer, and Frank [Akademischer Betreuer] Puppe. "Data Mining Methods for Medical Diagnosis : Test Selection, Subgroup Discovery, and Contrained Clustering / Marianne Larissa Mueller. Gutachter: Stefan Kramer ; Frank Puppe. Betreuer: Stefan Kramer." München : Universitätsbibliothek der TU München, 2012. http://d-nb.info/1024964264/34.

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8

Li, Rui [Verfasser], Burkhard [Akademischer Betreuer] [Gutachter] Rost, and Stefan [Gutachter] Kramer. "Data Mining and Machine Learning Methods for High-dimensional Patient Data in Dementia Research: Voxel Features Mining, Subgroup Discovery and Multi-view Learning / Rui Li ; Gutachter: Burkhard Rost, Stefan Kramer ; Betreuer: Burkhard Rost." München : Universitätsbibliothek der TU München, 2017. http://d-nb.info/1125018224/34.

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9

Domingue, Jean-Laurent. "Nurses’ Knowledge, Attitudes and Documentation Practices in a Context of HIV Criminalization: A Secondary Subgroup Analysis of Data from California, Florida, New York, and Texas Nurses." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/35570.

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Under international legal norms, HIV criminalization is considered to be an overly broad use of criminal law. In the United States, at least 33 states have HIV-specific criminal laws. Data from California, Florida, New York, and Texas nurses provided exemplars from different HIV-related criminal law approaches and the impact of those laws on nurses’ practices. Nurses who cared for patients who expressed fears or concerns about HIV criminalization or patients who had been arrested for HIV-related crimes were more likely to correctly identify the presence or absence of HIV-specific laws in the s
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10

PASTOR, ELIANA. "Pattern-based algorithms for Explainable AI." Doctoral thesis, Politecnico di Torino, 2021. http://hdl.handle.net/11583/2942116.

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11

Belfodil, Adnene. "Exceptional model mining for behavioral data analysis." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEI086.

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Avec la prolifération rapide des plateformes de données qui récoltent des données relatives à plusieurs domaines tels que les données de gouvernements, d’éducation, d’environnement ou les données de notations de produits, plus de données sont disponibles en ligne. Ceci représente une opportunité sans égal pour étudier le comportement des individus et les interactions entre eux. Sur le plan politique, le fait de pouvoir interroger des ensembles de données de votes peut fournir des informations intéressantes pour les journalistes et les analystes politiques. En particulier, ce type de données pe
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12

Wesley, S. Scott. "Background data subgroups and career outcomes : some developmental influences on person job-matching." Diss., Georgia Institute of Technology, 1989. http://hdl.handle.net/1853/31065.

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13

Lütz, Elin. "Unsupervised machine learning to detect patient subgroups in electronic health records." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-251669.

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The use of Electronic Health Records (EHR) for reporting patient data has been widely adopted by healthcare providers. This data can encompass many forms of medical information such as disease symptoms, results from laboratory tests, ICD-10 classes and other information from patients. Structured EHR data is often high-dimensional and contain many missing values, which impose a complication to many computing problems. Detecting meaningful structures in EHR data could provide meaningful insights in diagnose detection and in development of medical decision support systems. In this work, a subset
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14

Hawken, Steven. "Methodological Approaches to Studying Risk Factors for Adverse Events Following Routine Vaccinations in the General Population and Vulnerable Subgroups of Individuals Using Health Administrative Data." Thesis, Université d'Ottawa / University of Ottawa, 2014. http://hdl.handle.net/10393/31774.

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Objectives: This thesis included 6 manuscripts which focused on the analysis of adverse events following immunization (AEFIs), including general health services utilization (emergency room (ER) visits and hospital admissions) and specific diagnoses (e.g. febrile convulsions). The main objectives of this research were: 1) To demonstrate the utility of the self-controlled case series (SCCS) design coupled with health administrative data for studying the safety of vaccines; 2) Introducing an innovative approach using relative incidence ratios (RIRs) within an SCCS analysis to identify risk fact
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15

Hammal, Mohamed Ali. "Contribution à la découverte de sous-groupes corrélés : Application à l’analyse des systèmes territoriaux et des réseaux alimentaires." Thesis, Lyon, 2020. http://www.theses.fr/2020LYSEI024.

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Mieux nourrir les villes en quantité et en qualité, notamment les grandes agglomérations, constitue un défi majeur dont la résolution passe par une meilleure compréhension des relations entre les populations urbaines et leur alimentation. A l’échelle des systèmes alimentaires urbains, on a besoin de diagnostics ciblant la disponibilité des ressources alimentaires croisée avec les profils socio-économiques des territoires et l’on manque d’outils et de méthodes pour appréhender de façon systématique les relations entre les bassins de consommation, l’offre et les comportements alimentaires. L’obj
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16

Tillberg, Anders. "A multidisciplinary risk assessment of dental restorative materials." Doctoral thesis, Umeå : Univ, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-1860.

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17

Bosc, Guillaume. "Anytime discovery of a diverse set of patterns with Monte Carlo tree search." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSEI074/document.

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La découverte de motifs qui caractérisent fortement une classe vis à vis d'une autre reste encore un problème difficile en fouille de données. La découverte de sous-groupes (Subgroup Discovery, SD) est une approche formelle de fouille de motifs qui permet la construction de classifieurs intelligibles mais surtout d'émettre des hypothèses sur les données. Cependant, cette approche fait encore face à deux problèmes majeurs : (i) comment définir des mesures de qualité appropriées pour caractériser l'intérêt d'un motif et (ii) comment sélectionner une méthode heuristique adaptée lorsqu’une énuméra
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18

Underwood, Marilyn. "The Relationship of 10th-Grade District Progress Monitoring Assessment Scores to Florida Comprehensive Assessment Test Scores in Reading and Mathematics for 2008-2009." Doctoral diss., University of Central Florida, 2010. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3845.

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The focus of this research was to investigate the use of a district created formative benchmark assessment in reading to predict student achievement for 10th-grade students on the Florida Comprehensive Assessment Test (FCAT) in one county in north central Florida. The purpose of the study was to provide information to high school principals and teachers to better understand how students were performing and learning and to maximize use of the formative district benchmark assessment in order to modify instruction and positively impact student achievement. This study expanded a prior limited stud
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19

Tseng, Jen Yu, and 曾仁佑. "Subgroup Data Analysis Using Survival Tree." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/24649969791940075527.

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碩士<br>國立清華大學<br>統計學研究所<br>104<br>In this thesis, that we adopt the subgroup analysis to right censored data depends on the method of Su et al. (2008). There are two methods that include Interaction Tree and using the random forest to estimate the importance of each covariate for the subgroup analysis. We try to exploit simulation and real data analysis to observe the performance of them. In real data analysis, we analyze the data of the patients with lung cancer and use their gene expression as the covariate. However, in the large number of covariate, the problem of the calculation speed of In
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20

Lemmerich, Florian. "Novel Techniques for Efficient and Effective Subgroup Discovery." Doctoral thesis, 2014. https://nbn-resolving.org/urn:nbn:de:bvb:20-opus-97812.

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Large volumes of data are collected today in many domains. Often, there is so much data available, that it is difficult to identify the relevant pieces of information. Knowledge discovery seeks to obtain novel, interesting and useful information from large datasets. One key technique for that purpose is subgroup discovery. It aims at identifying descriptions for subsets of the data, which have an interesting distribution with respect to a predefined target concept. This work improves the efficiency and effectiveness of subgroup discovery in different directions. For efficient exhaustive subgro
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21

Atzmüller, Martin. "Knowledge-Intensive Subgroup Mining - Techniques for Automatic and Interactive Discovery." Doctoral thesis, 2006. https://nbn-resolving.org/urn:nbn:de:bvb:20-opus-21004.

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Data mining has proved its significance in various domains and applications. As an important subfield of the general data mining task, subgroup mining can be used, e.g., for marketing purposes in business domains, or for quality profiling and analysis in medical domains. The goal is to efficiently discover novel, potentially useful and ultimately interesting knowledge. However, in real-world situations these requirements often cannot be fulfilled, e.g., if the applied methods do not scale for large data sets, if too many results are presented to the user, or if many of the discovered patterns
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22

Costa, Afonso José Ourives Marques da. "Handling Data Difficulty Factors via a Meta-Learning Approach." Master's thesis, 2020. http://hdl.handle.net/10316/92560.

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Trabalho de Projeto do Mestrado Integrado em Engenharia Biomédica apresentado à Faculdade de Ciências e Tecnologia<br>As aplicações de aprendizagem-máquina são desafiadas pelos fatores de complexidade dos dados. Estes são responsáveis pela degradação da qualidade dos dados, sendo que lidar com estes fatores é uma tarefa importante para evitar a degradação do desempenho de classificadores. Dentro dos fatores de complexidade, o desequilíbrio de classes, que é característico em diversas bases de dados biomédicas, normalmente é abordado com algoritmos de pré-processamento, que sã
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23

Wang, Xiaojing. "Bayesian Modeling Using Latent Structures." Diss., 2012. http://hdl.handle.net/10161/5848.

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<p>This dissertation is devoted to modeling complex data from the</p><p>Bayesian perspective via constructing priors with latent structures.</p><p>There are three major contexts in which this is done -- strategies for</p><p>the analysis of dynamic longitudinal data, estimating</p><p>shape-constrained functions, and identifying subgroups. The</p><p>methodology is illustrated in three different</p><p>interdisciplinary contexts: (1) adaptive measurement testing in</p><p>education; (2) emulation of computer models for vehicle crashworthiness; and (3) subgroup analyses based on biomarkers.</p><p>Ch
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24

Shen, Hua. "Statistical Methods for Life History Analysis Involving Latent Processes." Thesis, 2014. http://hdl.handle.net/10012/8496.

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Incomplete data often arise in the study of life history processes. Examples include missing responses, missing covariates, and unobservable latent processes in addition to right censoring. This thesis is on the development of statistical models and methods to address these problems as they arise in oncology and chronic disease. Methods of estimation and inference in parametric, weakly parametric and semiparametric settings are investigated. Studies of chronic diseases routinely sample individuals subject to conditions on an event time of interest. In epidemiology, for example, prevalent co
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25

Lee, Hsi-Yen, and 李錫諺. "Iterative clustering of gene expression data in search of subgroups of general population." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/4sadzd.

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