Littérature scientifique sur le sujet « Data Subgroup »
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Articles de revues sur le sujet "Data Subgroup"
Kavšek, Branko, et Nada Lavrač. « Using subgroup discovery to analyze the UK traffic data ». Advances in Methodology and Statistics 1, no 1 (1 janvier 2004) : 249–64. http://dx.doi.org/10.51936/zewh2294.
Texte intégralHuang, Xifen, Chaosong Xiong, Jinfeng Xu, Jianhua Shi et Jinhong Huang. « Mixture Modeling of Time-to-Event Data in the Proportional Odds Model ». Mathematics 10, no 18 (16 septembre 2022) : 3375. http://dx.doi.org/10.3390/math10183375.
Texte intégralMukherjee, Shubhabrata, Jesse Mez, Emily H. Trittschuh, Andrew J. Saykin, Laura E. Gibbons, David W. Fardo, Madeline Wessels et al. « Genetic data and cognitively defined late-onset Alzheimer’s disease subgroups ». Molecular Psychiatry 25, no 11 (4 décembre 2018) : 2942–51. http://dx.doi.org/10.1038/s41380-018-0298-8.
Texte intégralLötsch, Jörn, et 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 (20 décembre 2019) : 79. http://dx.doi.org/10.3390/ijms21010079.
Texte intégralEmery, William. « WOCE/TOGA Historical Oceanographic Data Subgroup ». Eos, Transactions American Geophysical Union 67, no 22 (1986) : 500. http://dx.doi.org/10.1029/eo067i022p00500-03.
Texte intégralPan, Yunzhi, Weidan Pu, Xudong Chen, Xiaojun Huang, Yan Cai, Haojuan Tao, Zhiming Xue et al. « Morphological Profiling of Schizophrenia : Cluster Analysis of MRI-Based Cortical Thickness Data ». Schizophrenia Bulletin 46, no 3 (4 janvier 2020) : 623–32. http://dx.doi.org/10.1093/schbul/sbz112.
Texte intégralShirrell, Matthew. « The Effects of Subgroup-Specific Accountability on Teacher Turnover and Attrition ». Education Finance and Policy 13, no 3 (juillet 2018) : 333–68. http://dx.doi.org/10.1162/edfp_a_00227.
Texte intégralKoopman, Laura, Geert J. M. G. van der Heijden, Arno W. Hoes, Diederick E. Grobbee et Maroeska M. Rovers. « Empirical comparison of subgroup effects in conventional and individual patient data meta-analyses ». International Journal of Technology Assessment in Health Care 24, no 03 (juillet 2008) : 358–61. http://dx.doi.org/10.1017/s0266462308080471.
Texte intégralNanlin Jin, Peter Flach, Tom Wilcox, Royston Sellman, Joshua Thumim et Arno Knobbe. « Subgroup Discovery in Smart Electricity Meter Data ». IEEE Transactions on Industrial Informatics 10, no 2 (mai 2014) : 1327–36. http://dx.doi.org/10.1109/tii.2014.2311968.
Texte intégralTsai, Kao-Tai, et Karl Peace. « Analysis of Subgroup Data of Clinical Trials ». Journal of Causal Inference 1, no 2 (10 septembre 2013) : 193–207. http://dx.doi.org/10.1515/jci-2012-0008.
Texte intégralThèses sur le sujet "Data Subgroup"
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.
Texte intégralAtzmüller, 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.
Texte intégralBelfodil, Aimene. « An order theoretic point-of-view on subgroup discovery ». Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEI078.
Texte intégralAs the title of this dissertation may suggest, the aim of this thesis is to provide an order-theoretic point of view on the task of subgroup discovery. Subgroup discovery is the automatic task of discovering interesting hypotheses in databases. That is, given a database, the hypothesis space the analyst wants to explore and a formal way of how the analyst gauges the quality of the hypotheses (e.g. a quality measure); the automated task of subgroup discovery aims to extract the interesting hypothesis w.r.t. these parameters. In order to elaborate fast and efficient algorithms for subgroup discovery, one should understand the underlying properties of the hypothesis space on the one hand and the properties of its quality measure on the other. In this thesis, we extend the state-of-the-art by: (i) providing a unified view of the hypotheses space behind subgroup discovery using the well-founded mathematical tool of order theory, (ii) proposing the new hypothesis space of conjunction of linear inequalities in numerical databases and the algorithms enumerating its elements and (iii) proposing an anytime algorithm for discriminative subgroup discovery on numerical datasets providing guarantees upon interruption
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/.
Texte intégralDoubleday, 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.
Texte intégralMueller, Marianne Larissa [Verfasser], Stefan [Akademischer Betreuer] Kramer et 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.
Texte intégralLi, Rui [Verfasser], Burkhard [Akademischer Betreuer] [Gutachter] Rost et 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.
Texte intégralDomingue, 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.
Texte intégralBelfodil, Adnene. « Exceptional model mining for behavioral data analysis ». Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEI086.
Texte intégralWith the rapid proliferation of data platforms collecting and curating data related to various domains such as governments data, education data, environment data or product ratings, more and more data are available online. This offers an unparalleled opportunity to study the behavior of individuals and the interactions between them. In the political sphere, being able to query datasets of voting records provides interesting insights for data journalists and political analysts. In particular, such data can be leveraged for the investigation of exceptionally consensual/controversial topics. Consider data describing the voting behavior in the European Parliament (EP). Such a dataset records the votes of each member (MEP) in voting sessions held in the parliament, as well as information on the parliamentarians (e.g., gender, national party, European party alliance) and the sessions (e.g., topic, date). This dataset offers opportunities to study the agreement or disagreement of coherent subgroups, especially to highlight unexpected behavior. It is to be expected that on the majority of voting sessions, MEPs will vote along the lines of their European party alliance. However, when matters are of interest to a specific nation within Europe, alignments may change and agreements can be formed or dissolved. For instance, when a legislative procedure on fishing rights is put before the MEPs, the island nation of the UK can be expected to agree on a specific course of action regardless of their party alliance, fostering an exceptional agreement where strong polarization exists otherwise. In this thesis, we aim to discover such exceptional (dis)agreement patterns not only in voting data but also in more generic data, called behavioral data, which involves individuals performing observable actions on entities. We devise two novel methods which offer complementary angles of exceptional (dis)agreement in behavioral data: within and between groups. These two approaches called Debunk and Deviant, ideally, enables the implementation of a sufficiently comprehensive tool to highlight, summarize and analyze exceptional comportments in behavioral data. We thoroughly investigate the qualitative and quantitative performances of the devised methods. Furthermore, we motivate their usage in the context of computational journalism
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.
Texte intégralLivres sur le sujet "Data Subgroup"
Atzmüller, Martin. Knowledge-intensive subgroup mining : Techniques for automatic and interactive discovery. Berlin : Aka, Akademische Verlagsgsellschaft, 2007.
Trouver le texte intégralOffice, General Accounting. Decennial census : Methods for collecting and reporting Hispanic subgroup data need refinement : report to Congressional Requesters. [Washington, D.C.] : GAO, 2003.
Trouver le texte intégralSiek-Toon, Khoo, Goff Ginger Nelson et Educational Resources Information Center (U.S.), dir. Multidimensional description of subgroup differences in mathematics achievement data from the 1992 National Assessment of Educational Progress : Draft. Los Angeles CA : National Center for Research on Evaluation, Standards, and Student Testing, 1994.
Trouver le texte intégralWright, 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. Washington : U.S. G.P.O., 1989.
Trouver le texte intégral1932-, Kameny Iris, United States. Defense Modeling and Simulation Office. Data and Repositories Technology Working Group., National Defense Research Institute (U.S.) et United States. Dept. of Defense., dir. 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. Santa Monica, CA : Rand, 1995.
Trouver le texte intégralHajat, Anjum. Health outcomes among Hispanic subgroups : Data from the National Health Interview Survey, 1992-95. [Hyattsville, Md.] (6525 Belcrest Rd., Hyattsville 20782-2003) : [U.S. Dept. of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics, 2000.
Trouver le texte intégralUnited States. Substance Abuse and Mental Health Services Administration. Office of Applied Studies., dir. Prevalence of substance use among racial and ethnic subgroups in the United States, 1991-1993. Rockville, Md. (5600 Fishers Lane, Rm. 16-105, Rockville 20857) : Dept. of Health and Human Services, Substance Abuse and Mental Health Services Administration, Office of Applied Studies, 1998.
Trouver le texte intégralAtzmuller, Martin. Knowledge-Intensive Subgroup Mining : Techniques for Automatic and Interactive Discovery - Volume 307 Dissertations in Artificial Intelligence - Infix ... in Artificial Intelligence). IOS Press, 2007.
Trouver le texte intégralTran, Thanh V., et 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.
Texte intégralProctor, Kim. Measuring Group Consciousness. Sous la direction de Lonna Rae Atkeson et R. Michael Alvarez. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780190213299.013.33.
Texte intégralChapitres de livres sur le sujet "Data Subgroup"
Cleophas, Ton J., et Aeilko H. Zwinderman. « Subgroup Analysis ». Dans Understanding Clinical Data Analysis, 141–56. Cham : Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39586-9_7.
Texte intégralKlösgen, W. « Subgroup Mining ». Dans Computational Intelligence in Data Mining, 39–49. Vienna : Springer Vienna, 2000. http://dx.doi.org/10.1007/978-3-7091-2588-5_2.
Texte intégralKim, Ju Han. « Gene Set Approaches and Prognostic Subgroup Prediction ». Dans Genome Data Analysis, 135–57. Singapore : Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-1942-6_8.
Texte intégralLavrač, Nada. « Subgroup Discovery Techniques and Applications ». Dans Advances in Knowledge Discovery and Data Mining, 2–14. Berlin, Heidelberg : Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11430919_2.
Texte intégralAtzmueller, Martin, Juergen Mueller et Martin Becker. « Exploratory Subgroup Analytics on Ubiquitous Data ». Dans Lecture Notes in Computer Science, 1–20. Cham : Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-14723-9_1.
Texte intégralGähler, Franz. « Computer checking of the subgroup data ». Dans International Tables for Crystallography, 27–28. Chester, England : International Union of Crystallography, 2006. http://dx.doi.org/10.1107/97809553602060000540.
Texte intégralGähler, Franz. « Computer checking of the subgroup data ». Dans International Tables for Crystallography, 25–26. Chester, England : International Union of Crystallography, 2011. http://dx.doi.org/10.1107/97809553602060000792.
Texte intégralDzyuba, Vladimir, et Matthijs van Leeuwen. « Interactive Discovery of Interesting Subgroup Sets ». Dans Advances in Intelligent Data Analysis XII, 150–61. Berlin, Heidelberg : Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41398-8_14.
Texte intégralMillot, Alexandre, Rémy Cazabet et Jean-François Boulicaut. « Optimal Subgroup Discovery in Purely Numerical Data ». Dans Advances in Knowledge Discovery and Data Mining, 112–24. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47436-2_9.
Texte intégralCleophas, Ton J., et Aeilko H. Zwinderman. « Subgroup Characteristics Assessed as Dependent Adverse Effects ». Dans Analysis of Safety Data of Drug Trials, 183–93. Cham : Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05804-3_14.
Texte intégralActes de conférences sur le sujet "Data Subgroup"
Lemmerich, Florian, et Frank Puppe. « Local Models for Expectation-Driven Subgroup Discovery ». Dans 2011 IEEE 11th International Conference on Data Mining (ICDM). IEEE, 2011. http://dx.doi.org/10.1109/icdm.2011.94.
Texte intégralYang, Xi, Yuan Zhang et Min Chi. « Time-aware Subgroup Matrix Decomposition : Imputing Missing Data Using Forecasting Events ». Dans 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622436.
Texte intégralLiu, Jing, Yu Jiang, Zechao Li, Xi Zhang et Hanqing Lu. « Domain-sensitive Recommendation with user-item subgroup analysis ». Dans 2016 IEEE 32nd International Conference on Data Engineering (ICDE). IEEE, 2016. http://dx.doi.org/10.1109/icde.2016.7498377.
Texte intégralLijffijt, Jefrey, Bo Kang, Wouter Duivesteijn, Kai Puolamaki, Emilia Oikarinen et Tijl De Bie. « Subjectively Interesting Subgroup Discovery on Real-Valued Targets ». Dans 2018 IEEE 34th International Conference on Data Engineering (ICDE). IEEE, 2018. http://dx.doi.org/10.1109/icde.2018.00148.
Texte intégralMathonat, Romain, Diana Nurbakova, Jean-Francois Boulicaut et Mehdi Kaytoue. « Anytime Subgroup Discovery in High Dimensional Numerical Data ». Dans 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2021. http://dx.doi.org/10.1109/dsaa53316.2021.9564223.
Texte intégralTrabold, Daniel, et Henrik Grosskreutz. « Parallel subgroup discovery on computing clusters — ; First results ». Dans 2013 IEEE International Conference on Big Data. IEEE, 2013. http://dx.doi.org/10.1109/bigdata.2013.6691625.
Texte intégralPurucker, Lennart, Felix Stamm, Florian Lemmerich et Joeran Beel. « Estimating the Pruned Search Space Size of Subgroup Discovery ». Dans 2022 IEEE International Conference on Data Mining (ICDM). IEEE, 2022. http://dx.doi.org/10.1109/icdm54844.2022.00147.
Texte intégralPadillo, F., J. M. Luna et S. Ventura. « Subgroup discovery on big data : Pruning the search space on exhaustive search algorithms ». Dans 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7840799.
Texte intégralMeeng, Marvin, Wouter Duivesteijn et Arno Knobbe. « ROCsearch — An ROC-guided Search Strategy for Subgroup Discovery ». Dans Proceedings of the 2014 SIAM International Conference on Data Mining. Philadelphia, PA : Society for Industrial and Applied Mathematics, 2014. http://dx.doi.org/10.1137/1.9781611973440.81.
Texte intégralShukla, Piyush Kumar, Pradeep Rusiya, Deepak Agrawal, Lata Chhablani et Balwant Singh Raghuwanshi. « Multiple Subgroup Data Compression Technique Based on Huffman Coding ». Dans 2009 First International Conference on Computational Intelligence, Communication Systems and Networks (CICSYN). IEEE, 2009. http://dx.doi.org/10.1109/cicsyn.2009.86.
Texte intégralRapports d'organisations sur le sujet "Data Subgroup"
Kim, Kang Seog. SUBGR : A Program to Generate Subgroup Data for the Subgroup Resonance Self-Shielding Calculation. Office of Scientific and Technical Information (OSTI), juin 2016. http://dx.doi.org/10.2172/1261346.
Texte intégralWu, Ling, Tao Zhang, Yao Wang, Xiao Ke Wu, Tin Chiu Li, Pui Wah Chung et Chi Chiu Wang. 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, janvier 2022. http://dx.doi.org/10.37766/inplasy2022.1.0052.
Texte intégralWells, Aaron, Tracy Christopherson, Gerald Frost, Matthew Macander, Susan Ives, Robert McNown et Erin Johnson. Ecological land survey and soils inventory for Katmai National Park and Preserve, 2016–2017. National Park Service, septembre 2021. http://dx.doi.org/10.36967/nrr-2287466.
Texte intégralCaulfield, Laura E., Wendy L. Bennett, Susan M. Gross, Kristen M. Hurley, S. Michelle Ogunwole, Maya Venkataramani, Jennifer L. Lerman, Allen Zhang, Ritu Sharma et Eric B. Bass. Maternal and Child Outcomes Associated With the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC). Agency for Healthcare Research and Quality (AHRQ), avril 2022. http://dx.doi.org/10.23970/ahrqepccer253.
Texte intégralChou, Roger, Rongwei Fu, Tracy Dana, Miranda Pappas, Erica Hart et Kimberly M. Mauer. Interventional Treatments for Acute and Chronic Pain : Systematic Review. Agency for Healthcare Research and Quality (AHRQ), septembre 2021. http://dx.doi.org/10.23970/ahrqepccer247.
Texte intégralSpitzer, Sonja, Vanessa di Lego, Angela Greulich et Raya Muttarak. A demographic perspective on human wellbeing : Concepts, measurement and population heterogeneity. Verlag der Österreichischen Akademie der Wissenschaften, septembre 2021. http://dx.doi.org/10.1553/populationyearbook2021.int01.
Texte intégralChou, Roger, Jesse Wagner, Azrah Y. Ahmed, Ian Blazina, Erika Brodt, David I. Buckley, Tamara P. Cheney et al. Treatments for Acute Pain : A Systematic Review. Agency for Healthcare Research and Quality (AHRQ), décembre 2020. http://dx.doi.org/10.23970/ahrqepccer240.
Texte intégralWu, Bin, Lixia Guo, Kaikai Zhen et Chao Sun. Diagnostic and prognostic value of miRNAs in hepatoblastoma : A systematic review with meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, novembre 2021. http://dx.doi.org/10.37766/inplasy2021.11.0045.
Texte intégralShumway, Dean A., Kimberly S. Corbin, Magdoleen H. Farah, Kelly E. Viola, Tarek Nayfeh, Samer Saadi, Vishal Shah et al. Partial Breast Irradiation for Breast Cancer. Agency for Healthcare Research and Quality (AHRQ), janvier 2023. http://dx.doi.org/10.23970/ahrqepccer259.
Texte intégralMobley, Erin M., Diana J. Moke, Joel Milam, Carol Y. Ochoa, Julia Stal, Nosa Osazuwa, Maria Bolshakova et al. Disparities and Barriers to Pediatric Cancer Survivorship Care. Agency for Healthcare Research and Quality (AHRQ), mars 2021. http://dx.doi.org/10.23970/ahrqepctb39.
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