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Relevant bibliographies by topics / Data Subgroup

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Academic literature on the topic 'Data Subgroup'

Author: Grafiati

Published: 14 March 2023

Last updated: 31 July 2025

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Journal articles on the topic "Data Subgroup"

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Ma, Jianglei, Huijie Zhang, Yuanyuan Zhang, and Guangming Wang. "Construction of gene subgroups of Crohn disease based on transcriptome data." Medicine 102, no. 31 (2023): e34482. http://dx.doi.org/10.1097/md.0000000000034482.

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Background: The global prevalence of Crohn disease (CD), a chronic inflammatory disease of the intestine, has been increasing; however, the etiology and pathogenesis of this disease have not been fully elucidated. Therefore, in the present study, we aimed to better understand the molecular mechanisms underlying CD to aid the development of novel therapeutic strategies for this condition. Methods: Based on the transcriptome data from patients with CD, this study used an unsupervised learning method to construct gene co-expression molecular subgroups and the R and SPSS software to identify the b

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Kavšek, Branko, and Nada Lavrač. "Using subgroup discovery to analyze the UK traffic data." Advances in Methodology and Statistics 1, no. 1 (2004): 249–64. http://dx.doi.org/10.51936/zewh2294.

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Rule learning is typically used in solving classification and prediction tasks. However, learning of classification rules can be adapted also to subgroup discovery. Such an adaptation has already been done for the CN2 rule learning algorithm. In previous work this new algorithm, called CN2-SD, has been described in detail and applied to the well known UCI data sets. This paper summarizes the modifications needed for the adaptation of the CN2 rule learner to subgroup discovery and presents its application to a real-life data set - the UK traffic data - confirming its appropriateness for subgrou

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Huang, Xifen, Chaosong Xiong, Jinfeng Xu, Jianhua Shi, and Jinhong Huang. "Mixture Modeling of Time-to-Event Data in the Proportional Odds Model." Mathematics 10, no. 18 (2022): 3375. http://dx.doi.org/10.3390/math10183375.

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Subgroup analysis with survival data are most essential for detailed assessment of the risks of medical products in heterogeneous population subgroups. In this paper, we developed a semiparametric mixture modeling strategy in the proportional odds model for simultaneous subgroup identification and regression analysis of survival data that flexibly allows the covariate effects to differ among several subgroups. Neither the membership or the subgroup-specific covariate effects are known a priori. The nonparametric maximum likelihood method together with a pair of MM algorithms with monotone asce

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Mukherjee, Shubhabrata, Jesse Mez, Emily H. Trittschuh, et al. "Genetic data and cognitively defined late-onset Alzheimer’s disease subgroups." Molecular Psychiatry 25, no. 11 (2018): 2942–51. http://dx.doi.org/10.1038/s41380-018-0298-8.

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Abstract Categorizing people with late-onset Alzheimer’s disease into biologically coherent subgroups is important for personalized medicine. We evaluated data from five studies (total n = 4050, of whom 2431 had genome-wide single-nucleotide polymorphism (SNP) data). We assigned people to cognitively defined subgroups on the basis of relative performance in memory, executive functioning, visuospatial functioning, and language at the time of Alzheimer’s disease diagnosis. We compared genotype frequencies for each subgroup to those from cognitively normal elderly controls. We focused on APOE and

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Sidorenkov, Andrey. "Informal subgroups in work groups: size and relations in subgroups." Bulletin of the Donetsk National University. Series D: Philology and Psychology 1 (February 27, 2025): 146–58. https://doi.org/10.5281/zenodo.14934698.

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The work examines the connection between the size of informal subgroups that arise in production groups and some relationship phenomena (interpersonal and subgroup trust, interpersonal and subgroup identity, interpersonal and subgroup conflict) within these subgroups. It also compares the convexity of the corresponding relationship phenomena in informal dyads and larger subgroups. The data from two empirical studies on different samples of work groups (N=37 and N=42) were used. In these groups, 65 and 71 informal subgroups, respectively, were found. The size of informal subgroups is significan

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Lötsch, Jörn, and Alfred Ultsch. "Current Projection Methods-Induced Biases at Subgroup Detection for Machine-Learning Based Data-Analysis of Biomedical Data." International Journal of Molecular Sciences 21, no. 1 (2019): 79. http://dx.doi.org/10.3390/ijms21010079.

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Advances in flow cytometry enable the acquisition of large and high-dimensional data sets per patient. Novel computational techniques allow the visualization of structures in these data and, finally, the identification of relevant subgroups. Correct data visualizations and projections from the high-dimensional space to the visualization plane require the correct representation of the structures in the data. This work shows that frequently used techniques are unreliable in this respect. One of the most important methods for data projection in this area is the t-distributed stochastic neighbor e

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Atzmueller, Martin, and Frank Puppe. "Semi-Automatic Visual Subgroup Mining using VIKAMINE." JUCS - Journal of Universal Computer Science 11, no. (11) (2005): 1752–65. https://doi.org/10.3217/jucs-011-11-1752.

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Visual mining methods enable the direct integration of the user to overcome major problems of automatic data mining methods, e.g., the presentation of uninteresting results, lack of acceptance of the discovered findings, or limited confidence in these. We present a novel subgroup mining approach for explorative and descriptive data mining implemented in the VIKAMINE system. We propose several integrated visualization methods to support subgroup mining. Furthermore, we describe three case studies using data from fielded systems in the medical domain.

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Khadirnaikar, Seema, Sudhanshu Shukla, and S. R. M. Prasanna. "Integration of pan-cancer multi-omics data for novel mixed subgroup identification using machine learning methods." PLOS ONE 18, no. 10 (2023): e0287176. http://dx.doi.org/10.1371/journal.pone.0287176.

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Cancer is a heterogeneous disease, and patients with tumors from different organs can share similar epigenetic and genetic alterations. Therefore, it is crucial to identify the novel subgroups of patients with similar molecular characteristics. It is possible to propose a better treatment strategy when the heterogeneity of the patient is accounted for during subgroup identification, irrespective of the tissue of origin. This work proposes a machine learning (ML) based pipeline for subgroup identification in pan-cancer. Here, mRNA, miRNA, DNA methylation, and protein expression features from pa

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An, Shengli, Peter Zhang, and Hong-Bin Fang. "Subgroup Identification in Survival Outcome Data Based on Concordance Probability Measurement." Mathematics 11, no. 13 (2023): 2855. http://dx.doi.org/10.3390/math11132855.

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Identifying a subgroup of patients who may have an enhanced treatment effect in a randomized clinical trial has received increasing attention recently. For time-to-event outcomes, it is a challenge to define the effectiveness of a treatment and to choose a cutoff time point for identifying subgroup membership, especially in trials in which the two treatment arms do not differ in overall survival. In this paper, we propose a mixture cure model to identify a subgroup for a new treatment that was compared to a classical treatment (or placebo) in a randomized clinical trial with respect to surviva

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Pan, Yunzhi, Weidan Pu, Xudong Chen, et al. "Morphological Profiling of Schizophrenia: Cluster Analysis of MRI-Based Cortical Thickness Data." Schizophrenia Bulletin 46, no. 3 (2020): 623–32. http://dx.doi.org/10.1093/schbul/sbz112.

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Abstract The diagnosis of schizophrenia is thought to embrace several distinct subgroups. The manifold entities in a single clinical patient group increase the variance of biological measures, deflate the group-level estimates of causal factors, and mask the presence of treatment effects. However, reliable neurobiological boundaries to differentiate these subgroups remain elusive. Since cortical thinning is a well-established feature in schizophrenia, we investigated if individuals (patients and healthy controls) with similar patterns of regional cortical thickness form naturally occurring mor

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Dissertations / Theses on the topic "Data Subgroup"

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

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Books on the topic "Data Subgroup"

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Atzmüller, Martin. Knowledge-intensive subgroup mining: Techniques for automatic and interactive discovery. Aka, Akademische Verlagsgsellschaft, 2007.

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Office, General Accounting. Decennial census: Methods for collecting and reporting Hispanic subgroup data need refinement : report to Congressional Requesters. GAO, 2003.

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Siek-Toon, Khoo, Goff Ginger Nelson, and Educational Resources Information Center (U.S.), eds. Multidimensional description of subgroup differences in mathematics achievement data from the 1992 National Assessment of Educational Progress: Draft. National Center for Research on Evaluation, Standards, and Student Testing, 1994.

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Wright, Thomas L. Chemical data for flows and feeder dikes of the Yakima Basalt Subgroup, Columbia River Basalt Group, Washington, Oregon, and Idaho, and their bearing on a petrogenetic model. U.S. G.P.O., 1989.

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1932-, Kameny Iris, United States. Defense Modeling and Simulation Office. Data and Repositories Technology Working Group., National Defense Research Institute (U.S.), and United States. Dept. of Defense., eds. Defense Modeling and Simulation Office Data and Repositories Technology Working Group (DRTWG) meetings held February 7-10, 1995, and additional task force and subgroup meetings held between July 1994 and February 1995. Rand, 1995.

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Hajat, Anjum. Health outcomes among Hispanic subgroups: Data from the National Health Interview Survey, 1992-95. [U.S. Dept. of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics, 2000.

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United States. Substance Abuse and Mental Health Services Administration. Office of Applied Studies., ed. Prevalence of substance use among racial and ethnic subgroups in the United States, 1991-1993. Dept. of Health and Human Services, Substance Abuse and Mental Health Services Administration, Office of Applied Studies, 1998.

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Atzmuller, Martin. Knowledge-Intensive Subgroup Mining: Techniques for Automatic and Interactive Discovery - Volume 307 Dissertations in Artificial Intelligence - Infix ... in Artificial Intelligence). IOS Press, 2007.

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Tran, Thanh V., and Keith T. Chan. Applied Cross-Cultural Data Analysis for Social Work. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780190888510.001.0001.

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Applied Cross-Cultural Data Analysis for Social Work is a research guide which provides a hands-on approach for learning and understanding data analysis techniques for examining and interpreting data for the purpose of cultural group comparisons. This book aims to provide practical applications in statistical approaches of data analyses that are commonly used in cross-cultural research and evaluation. Readers are presented with step-by-step illustrations in the use of descriptive, bivariate, and multivariate statistics to compare cross-cultural populations using large-scale, population-based s

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Proctor, Kim. Measuring Group Consciousness. Edited by Lonna Rae Atkeson and R. Michael Alvarez. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780190213299.013.33.

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Although group consciousness is an important concept in explaining political behavior, both theoretical guidance on how to measure group consciousness and empirical consensus regarding its operationalization are lacking. This has the potential to lead to both diverging results and inaccurate empirical conclusions, which greatly limits the ability to understand the role that group consciousness plays in politics. Using data from Pew’s 2013 “Survey of LGBT Americans,” this analysis provides a foundation for measuring group consciousness using item response theory (IRT). Through an examination of

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Book chapters on the topic "Data Subgroup"

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Cleophas, Ton J., and Aeilko H. Zwinderman. "Subgroup Analysis." In Understanding Clinical Data Analysis. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39586-9_7.

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Klösgen, W. "Subgroup Mining." In Computational Intelligence in Data Mining. Springer Vienna, 2000. http://dx.doi.org/10.1007/978-3-7091-2588-5_2.

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Kim, Ju Han. "Gene Set Approaches and Prognostic Subgroup Prediction." In Genome Data Analysis. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-1942-6_8.

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Lavrač, Nada. "Subgroup Discovery Techniques and Applications." In Advances in Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11430919_2.

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Atzmueller, Martin, Juergen Mueller, and Martin Becker. "Exploratory Subgroup Analytics on Ubiquitous Data." In Lecture Notes in Computer Science. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-14723-9_1.

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Zilvinskis, John. "Performing Subgroup Analysis on Quantitative Data." In Higher Education: Handbook of Theory and Research. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-51930-7_10-1.

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Zilvinskis, John. "Performing Subgroup Analysis on Quantitative Data." In Higher Education: Handbook of Theory and Research. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-51930-7_10-2.

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Gähler, Franz. "Computer checking of the subgroup data." In International Tables for Crystallography. International Union of Crystallography, 2006. http://dx.doi.org/10.1107/97809553602060000540.

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Gähler, Franz. "Computer checking of the subgroup data." In International Tables for Crystallography. International Union of Crystallography, 2011. http://dx.doi.org/10.1107/97809553602060000792.

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Zilvinskis, John. "Performing Subgroup Analysis on Quantitative Data." In Higher Education: Handbook of Theory and Research. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-58698-9_10.

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Conference papers on the topic "Data Subgroup"

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Cheng, I.-Ling, Chan Hsu, Chantung Ku, Pei-Ju Lee, and Yihuang Kang. "Subgroup Analysis via Model-based Rule Forest." In 2024 IEEE International Conference on Information Reuse and Integration for Data Science (IRI). IEEE, 2024. http://dx.doi.org/10.1109/iri62200.2024.00063.

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Kuratomi, Alejandro, Zed Lee, Panayiotis Tsaparas, et al. "CounterFair: Group Counterfactuals for Bias Detection, Mitigation and Subgroup Identification." In 2024 IEEE International Conference on Data Mining (ICDM). IEEE, 2024. https://doi.org/10.1109/icdm59182.2024.00025.

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Jyoti, Ashutosh Litoriya, and Sriram Kailasam. "Improving Parallel Exhaustive Subgroup Discovery with Early Search Space Pruning." In 2024 IEEE 31st International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW). IEEE, 2024. https://doi.org/10.1109/hipcw63042.2024.00037.

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Libin, Alexander, Jonah Treitler, Tadas Vasaitis, and Yijun Shao. "Evaluating and Reducing Subgroup Disparity in AI Models Predicting Pediatric COVID-19 Test Outcomes." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825572.

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Giobergia, Flavio, Eliana Pastor, Luca de Alfaro, and Elena Baralis. "Detecting Interpretable Subgroup Drifts." In KDD '25: The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 2025. https://doi.org/10.1145/3690624.3709259.

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Fatima, Areej, Naveed Hussain, Muhammad Usman, Mobeen Aslam та Nadia Ramzan. "Algebraic Aspacts of ξ-Pythagorean fuzzy subgroup". У Resent Trends in Statistics Data Analytics. Air University, 2024. https://doi.org/10.62500/icrtsda.1.1.36.

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Li, Tianlin, Zhiming Li, Anran Li, et al. "Fairness via Group Contribution Matching." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/49.

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Fairness issues in Deep Learning models have recently received increasing attention due to their significant societal impact. Although methods for mitigating unfairness are constantly proposed, little research has been conducted to understand how discrimination and bias develop during the standard training process. In this study, we propose analyzing the contribution of each subgroup (i.e., a group of data with the same sensitive attribute) in the training process to understand the cause of such bias development process. We propose a gradient-based metric to assess training subgroup contributi

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Lemmerich, Florian, and Frank Puppe. "Local Models for Expectation-Driven Subgroup Discovery." In 2011 IEEE 11th International Conference on Data Mining (ICDM). IEEE, 2011. http://dx.doi.org/10.1109/icdm.2011.94.

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Pastor, Eliana, Elena Baralis, and Luca de Alfaro. "A Hierarchical Approach to Anomalous Subgroup Discovery." In 2023 IEEE 39th International Conference on Data Engineering (ICDE). IEEE, 2023. http://dx.doi.org/10.1109/icde55515.2023.00203.

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Yang, Xi, Yuan Zhang, and Min Chi. "Time-aware Subgroup Matrix Decomposition: Imputing Missing Data Using Forecasting Events." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622436.

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Reports on the topic "Data Subgroup"

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Kim, Kang Seog. SUBGR: A Program to Generate Subgroup Data for the Subgroup Resonance Self-Shielding Calculation. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1261346.

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Neudecker, Denise, Allan Carlson, Michael Grosskopf, et al. AIACHNE's contribution for Nuclear Energy Agency Working Party on International Nuclear Data Evaluation Co-operation Subgroup 50. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2406680.

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Neudecker, D., B. Pritychenko, Allan Carlson, et al. AIACHNE's contribution for Nuclear Energy Agency Working Party on International Nuclear Data Evaluation Co-operation Subgroup 50. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2407742.

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Botulinum Neurotoxin-Producing Clostridia, Working Group on. Report on Botulinum Neurotoxin-Producing Clostridia. Food Standards Agency, 2023. http://dx.doi.org/10.46756/sci.fsa.ozk974.

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In 1992 a working group of the UK Advisory Committee on the Microbiological Safety of Food presented a report on Vacuum Packaging and Associated Processes regarding the microbiological safety of chilled foods. The report supported subsequent guidance provided by the UK Food Standards Agency for the safe manufacture of vacuum packed and modified atmosphere packed chilled foods. In 2021 the ACMSF requested that a new subgroup should update and build on the 1992 report as well as considering, in addition to chilled foods, some foods that are intended to be stored at ambient temperatures. The new

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Leanna, Moron, Katherine Irimata, and Jennifer Parker. Comparison of Mental Health Estimates by Sociodemographic Characteristics in the Research and Development Survey 3 and the 2019 National Health Interview Survey. National Center for Health Statistics (U.S.), 2013. http://dx.doi.org/10.15620/cdc:128964.

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This report compares national and subgroup estimates of any level of major depressive disorder (depression) and generalized anxiety disorder (GAD) symptoms (mild, moderate, or severe) among the U.S. adult population from two data sources, the 2019 National Health Interview Survey (NHIS) and the third Research and Development Survey (RANDS 3).

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Haynes, Ben, Tahmnina Khan, Ruth Willis, Camilla Alexander-White, and Alan Boobis. Joint position paper from the Advisory Committee on Novel Foods and Processes (ACNFP) & Committee on Toxicity (COT) on establishing a provisional acceptable daily intake (ADI) for pure form (≥98%) cannabidiol (CBD) in foods, based on new evidence. Food Standards Agency, 2023. http://dx.doi.org/10.46756/sci.fsa.zcg392.

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Following the announcement by the FSA that from 31st March 2021, all CBD products on the market must have their safety assured by companies submitting a valid safety application, there have been a large number of CBD-related novel food applications. Consequently, a significant level of additional data has become available that requires review. A joint Subgroup of the ACNFP and COT was formed to address a series of questions in relation to the safety of CBD-containing and hemp-derived ingredients. The overarching aim of the Subgroup is to enable the FSA to perform risk assessments for CBD in fo

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Wu, Ling, Tao Zhang, Yao Wang, et al. Polymorphisms and premature ovarian insufficiency and failure: A comprehensive meta-analysis update, subgroup, ranking, and network analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.1.0052.

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Review question / Objective: Early identification of women potentially who develop POI and POF is essential for early screening and treatment to improve clinical outcomes. We aim to conduct a comprehensive meta-analysis update, subgroup, ranking and network analysis for all available genetic polymorphism and associated with the POI and POF risk. Information sources: Six electronic databases will be included such as PubMed, Web of Science, Embase, MEDLINE, WANFANG DATA, CNKI. Will contact with authors by emails when necessary.

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Leis and Walsh. L51575 Mechanics-Based Analysis of SCC in a Carbonate-Bicarbonate Environment. Pipeline Research Council International, Inc. (PRCI), 1988. http://dx.doi.org/10.55274/r0010306.

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Stress-corrosion cracking (SCC) occurs occasionally in line-pipe steels. Reviews of this cracking indicate that the environment is carbonate-bicarbonate and that the cracking is primarily intergranular. Research sponsored by the PRCI Line Pipe Research Supervisory Committee (LPRSC) has over the years identified metallurgical and electrochemical parameters as playing a role in the process. This work has also argued the significance of strain rate and its relationship to the service loading, given that dissolution is indicated as the controlling mechanism. While much has been learned about the m

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Wells, Aaron, Tracy Christopherson, Gerald Frost, et al. Ecological land survey and soils inventory for Katmai National Park and Preserve, 2016–2017. National Park Service, 2021. http://dx.doi.org/10.36967/nrr-2287466.

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This study was conducted to inventory, classify, and map soils and vegetation within the ecosystems of Katmai National Park and Preserve (KATM) using an ecological land survey (ELS) approach. The ecosystem classes identified in the ELS effort were mapped across the park, using an archive of Geo-graphic Information System (GIS) and Remote Sensing (RS) datasets pertaining to land cover, topography, surficial geology, and glacial history. The description and mapping of the landform-vegetation-soil relationships identified in the ELS work provides tools to support the design and implementation of

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Székely, Miguel. Policy Options for Poverty Alleviation. Inter-American Development Bank, 1997. http://dx.doi.org/10.18235/0010754.

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This work builds on previous research to develop a methodology that simplifies the identification of the best policy options for poverty alleviation in a given country. When a population can be divided into subgroups according to an easily identifiable characteristic, the problem of alleviating poverty through a targeted mechanism can be viewed as one of choosing of three options: (i) provoking a marginal change in the average income of certain subgroups; (ii) changing the resource distribution within marginal subgroups; and (iii) generating a marginal change in the differences between the sub

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