Gotowe bibliografie tematyczne / Data mining

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  1. Artykuły w czasopismach
  2. Rozprawy doktorskie
  3. Książki
  4. Części książek
  5. Streszczenia konferencji
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Gotowa bibliografia na temat „Data mining”

Autor: Grafiati
Data publikacji: 4 czerwca 2021
Data aktualizacji: 25 lipca 2025

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Artykuły w czasopismach na temat "Data mining"

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PYLYPIUK, Tetiana, and Viktor SHCHYRBA. "DATA MINING METHODS." Collection of scientific papers Kamianets-Podilsky Ivan Ohienko National University Pedagogical series 29 (December 14, 2023): 7–10. http://dx.doi.org/10.32626/2307-4507.2023-29.7-10.

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Research is devoted to Data Mining methods. A comparison of classical and mathematical and statistical methods of data analysis was made. One of the variants of correlation analysis method for intelligent data analysis is proposed and described in an argumentative manner. The question of applying different methodologies for Data Mining is actual. Classically, the following methods of knowledge discovery and analysis are offered in Data Mining: classification; regression; forecasting time sequences (series); clustering; association. As mathematical and statistical methods of analysis in applied
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Shah Neha K, Shah Neha K. "Introduction of Data mining and an Analysis of Data mining Techniques." Indian Journal of Applied Research 3, no. 5 (2011): 137–39. http://dx.doi.org/10.15373/2249555x/may2013/41.

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Rakholiya, Kalpesh R., and Dr Dhaval Kathiriya. "Data Mining for Moving Object Data." Indian Journal of Applied Research 2, no. 3 (2011): 111–13. http://dx.doi.org/10.15373/2249555x/dec2012/34.

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Chomboon, K., N. Kaoungku, K. Kerdprasop, and N. Kerdprasop. "Data Mining in Semantic Web Data." International Journal of Computer Theory and Engineering 6, no. 6 (2014): 472–75. http://dx.doi.org/10.7763/ijcte.2014.v6.912.

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Загороднюк, П. А. "Data mining in Go." Vestnik of Russian New University. Series «Complex systems: models, analysis, management», no. 4 (January 10, 2022): 161–66. http://dx.doi.org/10.18137/rnu.v9187.21.04.p.161.

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Целью данной статьи является оценка языка программирования Go как инструмента для реализации методов data mining. Для этого проводится анализ задачи классификации и метода k-ближайших соседей, затем предлагается способ программирования данного метода и организации процесс управления и подготовки исходных данных. В заключение на основе проведенной работы делается вывод, насколько Go подходит для решения подобных задач, и есть ли потенциал для реализации остальных методов. The purpose of this article is to evaluate the Go programming language as a tool for implementing data mining methods. To do
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AVeselý. "Neural networks in data mining." Agricultural Economics (Zemědělská ekonomika) 49, No. 9 (2012): 427–31. http://dx.doi.org/10.17221/5427-agricecon.

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To posses relevant information is an inevitable condition for successful enterprising in modern business. Information could be parted to data and knowledge. How to gather, store and retrieve data is studied in database theory. In the knowledge engineering, there is in the centre of interest the knowledge and methods of its formalization and gaining are studied. Knowledge could be gained from experts, specialists in the area of interest, or it can be gained by induction from sets of data. Automatic induction of knowledge from data sets, usually stored in large databases, is called data mining.
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Stoffel, Kilian. "Web + Data Mining = Web Mining." HMD Praxis der Wirtschaftsinformatik 46, no. 4 (2009): 6–20. http://dx.doi.org/10.1007/bf03340377.

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M., Inbavalli. "An Intelligent Agent based Mining Techniques for Distributed Data Mining." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (2020): 610–17. http://dx.doi.org/10.5373/jardcs/v12sp4/20201527.

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Tsuta, Mizuki. "Data Mining." Nippon Shokuhin Kagaku Kogaku Kaishi 64, no. 6 (2017): 334–35. http://dx.doi.org/10.3136/nskkk.64.334.

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Rossini, Luiz Amelio Sodaite, Renan Ricardo de Polli Silva, Eder Carlos Salazar Sotto, and Liriane Soares De Araújo. "DATA MINING." Revista Interface Tecnológica 15, no. 2 (2018): 50–59. http://dx.doi.org/10.31510/infa.v15i2.486.

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A Mineração de Dados (Data Mining) deve ser entendida como um conjunto de esforços empregados para a descoberta de padrões de acordo com bases de dados. Dessa maneira, há condições de gerar conhecimento útil para a tomada de decisões, através de algoritmos computacionais que recebem fatos do mundo real (entrada) e devolvem um padrão de comportamento (saída), expresso como modelagem de um perfil. Sendo assim, o objetivo deste artigo é definir a Mineração de Dados e os conceitos inerentes a ela, bem como elencar algumas ferramentas utilizadas para extração de conhecimento a partir dos dados. A m
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Rozprawy doktorskie na temat "Data mining"

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Mrázek, Michal. "Data mining." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2019. http://www.nusl.cz/ntk/nusl-400441.

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The aim of this master’s thesis is analysis of the multidimensional data. Three dimensionality reduction algorithms are introduced. It is shown how to manipulate with text documents using basic methods of natural language processing. The goal of the practical part of the thesis is to process real-world data from the internet forum. Posted messages are transformed to the numerical representation, then to two-dimensional space and visualized. Later on, topics of the messages are discovered. In the last part, a few selected algorithms are compared.
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Payyappillil, Hemambika. "Data mining framework." Morgantown, W. Va. : [West Virginia University Libraries], 2005. https://etd.wvu.edu/etd/controller.jsp?moduleName=documentdata&jsp%5FetdId=3807.

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Thesis (M.S.)--West Virginia University, 2005<br>Title from document title page. Document formatted into pages; contains vi, 65 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 64-65).
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Abedjan, Ziawasch. "Improving RDF data with data mining." Phd thesis, Universität Potsdam, 2014. http://opus.kobv.de/ubp/volltexte/2014/7133/.

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Linked Open Data (LOD) comprises very many and often large public data sets and knowledge bases. Those datasets are mostly presented in the RDF triple structure of subject, predicate, and object, where each triple represents a statement or fact. Unfortunately, the heterogeneity of available open data requires significant integration steps before it can be used in applications. Meta information, such as ontological definitions and exact range definitions of predicates, are desirable and ideally provided by an ontology. However in the context of LOD, ontologies are often incomplete or simply not
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Liu, Tantan. "Data Mining over Hidden Data Sources." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1343313341.

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Taylor, Phillip. "Data mining of vehicle telemetry data." Thesis, University of Warwick, 2015. http://wrap.warwick.ac.uk/77645/.

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Driving a safety critical task that requires a high level of attention and workload from the driver. Despite this, people often perform secondary tasks such as eating or using a mobile phone, which increase workload levels and divert cognitive and physical attention from the primary task of driving. As well as these distractions, the driver may also be overloaded for other reasons, such as dealing with an incident on the road or holding conversations in the car. One solution to this distraction problem is to limit the functionality of in-car devices while the driver is overloaded. This can tak
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Sherikar, Vishnu Vardhan Reddy. "I2MAPREDUCE: DATA MINING FOR BIG DATA." CSUSB ScholarWorks, 2017. https://scholarworks.lib.csusb.edu/etd/437.

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This project is an extension of i2MapReduce: Incremental MapReduce for Mining Evolving Big Data . i2MapReduce is used for incremental big data processing, which uses a fine-grained incremental engine, a general purpose iterative model that includes iteration algorithms such as PageRank, Fuzzy-C-Means(FCM), Generalized Iterated Matrix-Vector Multiplication(GIM-V), Single Source Shortest Path(SSSP). The main purpose of this project is to reduce input/output overhead, to avoid incurring the cost of re-computation and avoid stale data mining results. Finally, the performance of i2MapReduce is anal
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Zhang, Nan. "Privacy-preserving data mining." [College Station, Tex. : Texas A&M University, 2006. http://hdl.handle.net/1969.1/ETD-TAMU-1080.

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Hulten, Geoffrey. "Mining massive data streams /." Thesis, Connect to this title online; UW restricted, 2005. http://hdl.handle.net/1773/6937.

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Büchel, Nina. "Faktorenvorselektion im Data Mining /." Berlin : Logos, 2009. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=019006997&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.

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Shao, Junming. "Synchronization Inspired Data Mining." Diss., lmu, 2011. http://nbn-resolving.de/urn:nbn:de:bvb:19-137356.

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Książki na temat "Data mining"

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Xu, Yue, Rosalind Wang, Anton Lord, et al., eds. Data Mining. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-8531-6.

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Dulli, Susi, Sara Furini, and Edmondo Peron. Data mining. Springer Milan, 2009. http://dx.doi.org/10.1007/978-88-470-1163-2.

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Stahlbock, Robert, Sven F. Crone, and Stefan Lessmann, eds. Data Mining. Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-1280-0.

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Islam, Rafiqul, Yun Sing Koh, Yanchang Zhao, et al., eds. Data Mining. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6661-1.

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Boo, Yee Ling, David Stirling, Lianhua Chi, Lin Liu, Kok-Leong Ong, and Graham Williams, eds. Data Mining. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0292-3.

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Nakhaeizadeh, Gholamreza, ed. Data Mining. Physica-Verlag HD, 1998. http://dx.doi.org/10.1007/978-3-642-86094-2.

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Aggarwal, Charu C. Data Mining. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14142-8.

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Runkler, Thomas A. Data Mining. Vieweg+Teubner, 2010. http://dx.doi.org/10.1007/978-3-8348-9353-6.

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Kantardzic, Mehmed. Data Mining. John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118029145.

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Le, Thuc D., Kok-Leong Ong, Yanchang Zhao, et al., eds. Data Mining. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-1699-3.

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Części książek na temat "Data mining"

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Freitas, Alex A., and Simon H. Lavington. "Data Mining." In Mining Very Large Databases with Parallel Processing. Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-5521-6_5.

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Rahman, Mirza I., and Robbert P. van Manen. "Data Mining." In Principles and Practice of Pharmaceutical Medicine. Wiley-Blackwell, 2010. http://dx.doi.org/10.1002/9781444325263.ch44.

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Du, Ke-Lin, and M. N. S. Swamy. "Data Mining." In Neural Networks and Statistical Learning. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5571-3_25.

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Chang, George, Marcus J. Healey, James A. M. McHugh, and Jason T. L. Wang. "Data Mining." In Mining the World Wide Web. Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1639-2_5.

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Pappa, Gisele L., and Alex A. Freitas. "Data Mining." In Natural Computing Series. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02541-9_2.

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Du, Ke-Lin, and M. N. S. Swamy. "Data Mining." In Neural Networks and Statistical Learning. Springer London, 2019. http://dx.doi.org/10.1007/978-1-4471-7452-3_30.

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Lee, Raymond S. T. "Data Mining." In Artificial Intelligence in Daily Life. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7695-9_4.

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Morzy, Tadeusz, and Maciej Zakrzewicz. "Data Mining." In Handbook on Data Management in Information Systems. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-24742-5_11.

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van der Aalst, Wil. "Data Mining." In Process Mining. Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-49851-4_4.

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Mohan, Chilukuri Krishna. "Data Mining." In Frontiers of Expert Systems. Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4509-5_9.

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Streszczenia konferencji na temat "Data mining"

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Song, Xiaoli, XiaoTong Wang, and Xiaohua Hu. "Semantic pattern mining for text mining." In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7840600.

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Agarwal, Shivam. "Data Mining: Data Mining Concepts and Techniques." In 2013 International Conference on Machine Intelligence and Research Advancement (ICMIRA). IEEE, 2013. http://dx.doi.org/10.1109/icmira.2013.45.

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Edelstein, Herb. "Data mining." In the seventh ACM SIGKDD international conference. ACM Press, 2001. http://dx.doi.org/10.1145/502512.502517.

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"Data mining." In 2015 International Symposium on Advanced Computing and Communication (ISACC). IEEE, 2015. http://dx.doi.org/10.1109/isacc.2015.7377334.

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DeWaal, Mindy. "Data Mining." In the 46th ACM Technical Symposium. ACM Press, 2015. http://dx.doi.org/10.1145/2676723.2693628.

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Ursyn, Anna. "Data mining." In ACM SIGGRAPH 2004 Art gallery. ACM Press, 2004. http://dx.doi.org/10.1145/1185884.1186011.

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Peñafiel, Myriam, Stefanie Vásquez, Diego Vásquez, Juan Zaldumbide, and Sergio Luján-Mora. "Data Mining and Opinion Mining." In the 2018 International Conference. ACM Press, 2018. http://dx.doi.org/10.1145/3274250.3274263.

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Yang, Tie-li, Ping-Bai, and Yu-Sheng Gong. "Spatial Data Mining Features between General Data Mining." In 2008 International Workshop on Geoscience and Remote Sensing (ETT and GRS). IEEE, 2008. http://dx.doi.org/10.1109/ettandgrs.2008.167.

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Ashok, Vikas, and Ravi Mukkamala. "Data mining without data." In the 10th annual ACM workshop. ACM Press, 2011. http://dx.doi.org/10.1145/2046556.2046578.

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"Session C: Dynamic data mining & data stream mining." In 2016 IEEE First International Conference on Data Stream Mining & Processing (DSMP). IEEE, 2016. http://dx.doi.org/10.1109/dsmp.2016.7583553.

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Raporty organizacyjne na temat "Data mining"

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Lee, K., H. Kargupta, B. G. Stafford, K. L. Buescher, and B. Ravindran. Data mining. Office of Scientific and Technical Information (OSTI), 1998. http://dx.doi.org/10.2172/334314.

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Kramer, Mitchell. Customer Data Mining. Patricia Seybold Group, 2004. http://dx.doi.org/10.1571/psgp5-27-04cc.

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Kramer, Mitchell. Data Mining at Work. Patricia Seybold Group, 2004. http://dx.doi.org/10.1571/psgp6-10-04cc.

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Brown, David A., John Hirdt, and Michal Herman. Data mining the EXFOR database. Office of Scientific and Technical Information (OSTI), 2013. http://dx.doi.org/10.2172/1122776.

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Lu, Xiaomeng, Robert Stambaugh, and Yu Yuan. Anomalies Abroad: Beyond Data Mining. National Bureau of Economic Research, 2017. http://dx.doi.org/10.3386/w23809.

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Davidson, George S., Jana Strasburg, David Stampf, et al. Data mining for ontology development. Office of Scientific and Technical Information (OSTI), 2010. http://dx.doi.org/10.2172/992328.

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Berry, Jonathan W., Vitus Joseph Leung, Cynthia Ann Phillips, et al. Statistically significant relational data mining :. Office of Scientific and Technical Information (OSTI), 2014. http://dx.doi.org/10.2172/1204082.

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Zdonik, Stanley B. Monitoring and Mining Data Streams. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada431589.

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Zdonik, Stan B. Monitoring and Mining Data Streams. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada419707.

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Zhan, Zhijun, and LiWu Chang. Privacy-Preserving Collaborative Data Mining. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada464602.

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