Academic literature on the topic 'Data Subgroup'
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Journal articles on the topic "Data Subgroup"
Kavšek, Branko, and Nada Lavrač. "Using subgroup discovery to analyze the UK traffic data." Advances in Methodology and Statistics 1, no. 1 (January 1, 2004): 249–64. http://dx.doi.org/10.51936/zewh2294.
Full textHuang, 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 (September 16, 2022): 3375. http://dx.doi.org/10.3390/math10183375.
Full textMukherjee, 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 (December 4, 2018): 2942–51. http://dx.doi.org/10.1038/s41380-018-0298-8.
Full textLötsch, 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 (December 20, 2019): 79. http://dx.doi.org/10.3390/ijms21010079.
Full textEmery, 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.
Full textPan, 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 (January 4, 2020): 623–32. http://dx.doi.org/10.1093/schbul/sbz112.
Full textShirrell, Matthew. "The Effects of Subgroup-Specific Accountability on Teacher Turnover and Attrition." Education Finance and Policy 13, no. 3 (July 2018): 333–68. http://dx.doi.org/10.1162/edfp_a_00227.
Full textKoopman, Laura, Geert J. M. G. van der Heijden, Arno W. Hoes, Diederick E. Grobbee, and 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 (July 2008): 358–61. http://dx.doi.org/10.1017/s0266462308080471.
Full textNanlin Jin, Peter Flach, Tom Wilcox, Royston Sellman, Joshua Thumim, and Arno Knobbe. "Subgroup Discovery in Smart Electricity Meter Data." IEEE Transactions on Industrial Informatics 10, no. 2 (May 2014): 1327–36. http://dx.doi.org/10.1109/tii.2014.2311968.
Full textTsai, Kao-Tai, and Karl Peace. "Analysis of Subgroup Data of Clinical Trials." Journal of Causal Inference 1, no. 2 (September 10, 2013): 193–207. http://dx.doi.org/10.1515/jci-2012-0008.
Full textDissertations / Theses on the topic "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.
Full textAtzmüller, 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.
Full textBelfodil, Aimene. "An order theoretic point-of-view on subgroup discovery." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEI078.
Full textAs 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/.
Full textDoubleday, 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.
Full textMueller, 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.
Full textLi, 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.
Full textDomingue, 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.
Full textBelfodil, Adnene. "Exceptional model mining for behavioral data analysis." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEI086.
Full textWith 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.
Full textBooks on the topic "Data Subgroup"
Atzmüller, Martin. Knowledge-intensive subgroup mining: Techniques for automatic and interactive discovery. Berlin: Aka, Akademische Verlagsgsellschaft, 2007.
Find full textOffice, General Accounting. Decennial census: Methods for collecting and reporting Hispanic subgroup data need refinement : report to Congressional Requesters. [Washington, D.C.]: GAO, 2003.
Find full textSiek-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. Los Angeles CA: National Center for Research on Evaluation, Standards, and Student Testing, 1994.
Find full textWright, 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.
Find full text1932-, 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. Santa Monica, CA: Rand, 1995.
Find full textHajat, 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.
Find full textUnited 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. 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.
Find full textAtzmuller, Martin. Knowledge-Intensive Subgroup Mining: Techniques for Automatic and Interactive Discovery - Volume 307 Dissertations in Artificial Intelligence - Infix ... in Artificial Intelligence). IOS Press, 2007.
Find full textTran, 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.
Full textProctor, 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.
Full textBook chapters on the topic "Data Subgroup"
Cleophas, Ton J., and Aeilko H. Zwinderman. "Subgroup Analysis." In Understanding Clinical Data Analysis, 141–56. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39586-9_7.
Full textKlösgen, W. "Subgroup Mining." In Computational Intelligence in Data Mining, 39–49. Vienna: Springer Vienna, 2000. http://dx.doi.org/10.1007/978-3-7091-2588-5_2.
Full textKim, Ju Han. "Gene Set Approaches and Prognostic Subgroup Prediction." In Genome Data Analysis, 135–57. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-1942-6_8.
Full textLavrač, Nada. "Subgroup Discovery Techniques and Applications." In Advances in Knowledge Discovery and Data Mining, 2–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11430919_2.
Full textAtzmueller, Martin, Juergen Mueller, and Martin Becker. "Exploratory Subgroup Analytics on Ubiquitous Data." In Lecture Notes in Computer Science, 1–20. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-14723-9_1.
Full textGähler, Franz. "Computer checking of the subgroup data." In International Tables for Crystallography, 27–28. Chester, England: International Union of Crystallography, 2006. http://dx.doi.org/10.1107/97809553602060000540.
Full textGähler, Franz. "Computer checking of the subgroup data." In International Tables for Crystallography, 25–26. Chester, England: International Union of Crystallography, 2011. http://dx.doi.org/10.1107/97809553602060000792.
Full textDzyuba, Vladimir, and Matthijs van Leeuwen. "Interactive Discovery of Interesting Subgroup Sets." In 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.
Full textMillot, Alexandre, Rémy Cazabet, and Jean-François Boulicaut. "Optimal Subgroup Discovery in Purely Numerical Data." In 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.
Full textCleophas, Ton J., and Aeilko H. Zwinderman. "Subgroup Characteristics Assessed as Dependent Adverse Effects." In 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.
Full textConference papers on the topic "Data Subgroup"
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.
Full textYang, 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.
Full textLiu, Jing, Yu Jiang, Zechao Li, Xi Zhang, and Hanqing Lu. "Domain-sensitive Recommendation with user-item subgroup analysis." In 2016 IEEE 32nd International Conference on Data Engineering (ICDE). IEEE, 2016. http://dx.doi.org/10.1109/icde.2016.7498377.
Full textLijffijt, Jefrey, Bo Kang, Wouter Duivesteijn, Kai Puolamaki, Emilia Oikarinen, and Tijl De Bie. "Subjectively Interesting Subgroup Discovery on Real-Valued Targets." In 2018 IEEE 34th International Conference on Data Engineering (ICDE). IEEE, 2018. http://dx.doi.org/10.1109/icde.2018.00148.
Full textMathonat, Romain, Diana Nurbakova, Jean-Francois Boulicaut, and Mehdi Kaytoue. "Anytime Subgroup Discovery in High Dimensional Numerical Data." In 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2021. http://dx.doi.org/10.1109/dsaa53316.2021.9564223.
Full textTrabold, Daniel, and Henrik Grosskreutz. "Parallel subgroup discovery on computing clusters — First results." In 2013 IEEE International Conference on Big Data. IEEE, 2013. http://dx.doi.org/10.1109/bigdata.2013.6691625.
Full textPurucker, Lennart, Felix Stamm, Florian Lemmerich, and Joeran Beel. "Estimating the Pruned Search Space Size of Subgroup Discovery." In 2022 IEEE International Conference on Data Mining (ICDM). IEEE, 2022. http://dx.doi.org/10.1109/icdm54844.2022.00147.
Full textPadillo, F., J. M. Luna, and S. Ventura. "Subgroup discovery on big data: Pruning the search space on exhaustive search algorithms." In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7840799.
Full textMeeng, Marvin, Wouter Duivesteijn, and Arno Knobbe. "ROCsearch — An ROC-guided Search Strategy for Subgroup Discovery." In 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.
Full textShukla, Piyush Kumar, Pradeep Rusiya, Deepak Agrawal, Lata Chhablani, and Balwant Singh Raghuwanshi. "Multiple Subgroup Data Compression Technique Based on Huffman Coding." In 2009 First International Conference on Computational Intelligence, Communication Systems and Networks (CICSYN). IEEE, 2009. http://dx.doi.org/10.1109/cicsyn.2009.86.
Full textReports on the topic "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), June 2016. http://dx.doi.org/10.2172/1261346.
Full textWu, Ling, Tao Zhang, Yao Wang, Xiao Ke Wu, Tin Chiu Li, Pui Wah Chung, and 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, January 2022. http://dx.doi.org/10.37766/inplasy2022.1.0052.
Full textWells, Aaron, Tracy Christopherson, Gerald Frost, Matthew Macander, Susan Ives, Robert McNown, and Erin Johnson. Ecological land survey and soils inventory for Katmai National Park and Preserve, 2016–2017. National Park Service, September 2021. http://dx.doi.org/10.36967/nrr-2287466.
Full textCaulfield, Laura E., Wendy L. Bennett, Susan M. Gross, Kristen M. Hurley, S. Michelle Ogunwole, Maya Venkataramani, Jennifer L. Lerman, Allen Zhang, Ritu Sharma, and 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), April 2022. http://dx.doi.org/10.23970/ahrqepccer253.
Full textChou, Roger, Rongwei Fu, Tracy Dana, Miranda Pappas, Erica Hart, and Kimberly M. Mauer. Interventional Treatments for Acute and Chronic Pain: Systematic Review. Agency for Healthcare Research and Quality (AHRQ), September 2021. http://dx.doi.org/10.23970/ahrqepccer247.
Full textSpitzer, Sonja, Vanessa di Lego, Angela Greulich, and Raya Muttarak. A demographic perspective on human wellbeing: Concepts, measurement and population heterogeneity. Verlag der Österreichischen Akademie der Wissenschaften, September 2021. http://dx.doi.org/10.1553/populationyearbook2021.int01.
Full textChou, 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), December 2020. http://dx.doi.org/10.23970/ahrqepccer240.
Full textWu, Bin, Lixia Guo, Kaikai Zhen, and 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, November 2021. http://dx.doi.org/10.37766/inplasy2021.11.0045.
Full textShumway, 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), January 2023. http://dx.doi.org/10.23970/ahrqepccer259.
Full textMobley, 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), March 2021. http://dx.doi.org/10.23970/ahrqepctb39.
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