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Relevant bibliographies by topics / Data Mining Techniques

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Academic literature on the topic 'Data Mining Techniques'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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Journal articles on the topic "Data Mining Techniques"

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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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Sherdiwala, Kainaz Bomi. "Data Mining Techniques in Stock Market." Indian Journal of Applied Research 4, no. 8 (2011): 327–29. http://dx.doi.org/10.15373/2249555x/august2014/82.

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S, Gowtham, and Karuppusamy S. "Review of Data Mining Classification Techniques." Bonfring International Journal of Software Engineering and Soft Computing 9, no. 2 (2019): 8–11. http://dx.doi.org/10.9756/bijsesc.9013.

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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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SAKURAI, Shigeaki. "Data Mining Techniques." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 21, no. 3 (2009): 348–57. http://dx.doi.org/10.3156/jsoft.21.3_348.

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Hegland, Markus. "Data mining techniques." Acta Numerica 10 (May 2001): 313–55. http://dx.doi.org/10.1017/s0962492901000058.

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Methods for knowledge discovery in data bases (KDD) have been studied for more than a decade. New methods are required owing to the size and complexity of data collections in administration, business and science. They include procedures for data query and extraction, for data cleaning, data analysis, and methods of knowledge representation. The part of KDD dealing with the analysis of the data has been termed data mining. Common data mining tasks include the induction of association rules, the discovery of functional relationships (classification and regression) and the exploration of groups o

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Han, Jiawei. "Data mining techniques." ACM SIGMOD Record 25, no. 2 (1996): 545. http://dx.doi.org/10.1145/235968.280351.

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D, Premalatha, Niveditha N, Poornima Vasanth V, Priyanka G. N, and Niveditha K B. "CANCER PREDICTION SYSTEM USING DATA MINING TECHNIQUES." International Journal of Current Engineering and Scientific Research 6, no. 6 (2019): 165–68. http://dx.doi.org/10.21276/ijcesr.2019.6.6.28.

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Delima, Allemar Jhone P. "Predicting Scholarship Grants Using Data Mining Techniques." International Journal of Machine Learning and Computing 9, no. 4 (2019): 513–19. http://dx.doi.org/10.18178/ijmlc.2019.9.4.834.

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Ramya, S., and D. Kalaivani. "Prevention of Diabetes Using Data Mining Techniques." International Journal of Science and Research (IJSR) 11, no. 6 (2022): 168–71. http://dx.doi.org/10.21275/sr22531200507.

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Dissertations / Theses on the topic "Data Mining Techniques"

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Tong, Suk-man Ivy. "Techniques in data stream mining." Click to view the E-thesis via HKUTO, 2005. http://sunzi.lib.hku.hk/hkuto/record/B34737376.

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Tong, Suk-man Ivy, and 湯淑敏. "Techniques in data stream mining." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B34737376.

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Burgess, Martin. "Transformation techniques in data mining." Thesis, University of East Anglia, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.410093.

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Transforming data is essential within data mining as a precursor to many applications such as rule induction and Multivariate Adaptive Regression Splines. The problems arising from the use of categorical valued data in rule induction are reduced confidence (accuracy), support and coverage. We introduce a technique called arcsin transformation where categorical valued data is replaced with numeric values. This technique has been used on a number of databases and has shown to be highly effective. Multivariate Adaptive Regression Splines, MARS, is a regression tool which attempts to approximate c

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Al-Hashemi, Idrees Yousef. "Applying data mining techniques over big data." Thesis, Boston University, 2013. https://hdl.handle.net/2144/21119.

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Thesis (M.S.C.S.) PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at open-help@bu.edu. Thank you.<br>The rapid development of information technology in recent decades means that data appear in a wide variety of formats — sensor data, tweets, photographs, raw data, and unstructured data. Statistics show that there were 800,000

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Zhou, Wubai. "Data Mining Techniques to Understand Textual Data." FIU Digital Commons, 2017. https://digitalcommons.fiu.edu/etd/3493.

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More than ever, information delivery online and storage heavily rely on text. Billions of texts are produced every day in the form of documents, news, logs, search queries, ad keywords, tags, tweets, messenger conversations, social network posts, etc. Text understanding is a fundamental and essential task involving broad research topics, and contributes to many applications in the areas text summarization, search engine, recommendation systems, online advertising, conversational bot and so on. However, understanding text for computers is never a trivial task, especially for noisy and ambiguous

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XIAO, XIN. "Data Mining Techniques for Complex User-Generated Data." Doctoral thesis, Politecnico di Torino, 2016. http://hdl.handle.net/11583/2644046.

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Nowadays, the amount of collected information is continuously growing in a variety of different domains. Data mining techniques are powerful instruments to effectively analyze these large data collections and extract hidden and useful knowledge. Vast amount of User-Generated Data (UGD) is being created every day, such as user behavior, user-generated content, user exploitation of available services and user mobility in different domains. Some common critical issues arise for the UGD analysis process such as the large dataset cardinality and dimensionality, the variable data distribution and i

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Yu, Congcong. "Parallelizing ensemble techniques for data mining." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/MQ59416.pdf.

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CID, DANTE JOSE ALEXANDRE. "DATA MINING WITH ROUGH SETS TECHNIQUES." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2000. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=7244@1.

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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>Esta dissertação investiga a utilização de Rough Sets no processo de descoberta de conhecimento em Bancos de Dados (KDD - Knowledge Discovery in Databases). O objetivo do trabalho foi avaliar o desempenho da técnica de Rough Sets na tarefa de Classificação de Dados. A Classificação é a tarefa da fase de Mineração de Dados que consiste na descoberta de regras de decisão, ou regras de inferência, que melhor representem um grupo de registros do banco de dados. O trabalho consistiu de cinco etapas principais: estudo sobre o p

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Al-Bataineh, Hussien Suleiman. "Islanding Detection Using Data Mining Techniques." Thesis, North Dakota State University, 2015. https://hdl.handle.net/10365/27634.

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Connection of the distributed generators (DGs), poses new challenges for operation and management of the distribution system. An important issue is that of islanding, where a part of the system gets disconnected from the DG. This thesis explores the use of several data-mining, and machine learning techniques to detect islanding. Several cases of islanding and non- islanding are simulated with a standard test-case: the IEEE 13 bus test distribution system. Different types of DGs are connected to the system and disturbances are introduced. Several classifiers are tested for their effectiveness i

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JANAKIRAMAN, KRISHNAMOORTHY. "ENTITY IDENTIFICATION USING DATA MINING TECHNIQUES." University of Cincinnati / OhioLINK, 2001. http://rave.ohiolink.edu/etdc/view?acc_num=ucin989852516.

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Books on the topic "Data Mining Techniques"

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Berry, Michael J. A. Data Mining Techniques. John Wiley & Sons, Ltd., 2004.

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Olson, David Louis. Advanced Data Mining Techniques. Springer-Verlag Berlin Heidelberg, 2008.

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Micheline, Kamber, ed. Data mining: Concepts and techniques. Morgan Kaufmann Publishers, 2001.

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Micheline, Kamber, ed. Data mining: Concepts and techniques. 3rd ed. Elsevier, 2011.

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Micheline, Kamber, ed. Data mining: Concepts and techniques. 2nd ed. Elsevier, 2006.

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Micheline, Kamber, ed. Data mining: Concepts and techniques. Elsevier, 2001.

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Appice, Annalisa, Anna Ciampi, Fabio Fumarola, and Donato Malerba. Data Mining Techniques in Sensor Networks. Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-5454-9.

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Shah, Ketan, Neepa Shah, Vinaya Sawant, and Neeraj Parolia. Practical Data Mining Techniques and Applications. Auerbach Publications, 2023. http://dx.doi.org/10.1201/9781003390220.

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Carugo, Oliviero, and Frank Eisenhaber, eds. Data Mining Techniques for the Life Sciences. Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2095-3.

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Carugo, Oliviero, and Frank Eisenhaber, eds. Data Mining Techniques for the Life Sciences. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3572-7.

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Book chapters on the topic "Data Mining Techniques"

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Rajola, Federico. "Data Mining Techniques." In Customer Relationship Management. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-24718-0_6.

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Ramos, Nuno M. M., João M. P. Q. Delgado, Ricardo M. S. F. Almeida, Maria L. Simões, and Sofia Manuel. "Data Mining Techniques." In Application of Data Mining Techniques in the Analysis of Indoor Hygrothermal Conditions. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22294-3_3.

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Rajola, Federico. "Data Mining Techniques." In Management for Professionals. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35554-7_8.

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Thuraisingham, Bhavani, Mohammad Mehedy Masud, Pallabi Parveen, and Latifur Khan. "Data Mining Techniques." In Big Data Analytics with Applications in Insider Threat Detection. Auerbach Publications, 2017. http://dx.doi.org/10.1201/9781315119458-4.

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Li, Deren, Shuliang Wang, and Deyi Li. "Methods and Techniques in SDM." In Spatial Data Mining. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-48538-5_5.

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Boire, Richard. "Applying Data Mining Techniques." In Data Mining for Managers. Palgrave Macmillan US, 2014. http://dx.doi.org/10.1057/9781137406194_12.

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Rani, Komal, Naveen Kumar, Manju Rani, and Kamal. "Intelligent data mining techniques." In Intelligent Data-Driven Techniques for Security of Digital Assets. CRC Press, 2025. https://doi.org/10.1201/9781003590439-8.

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Nenkova, Ani, and Kathleen McKeown. "A Survey of Text Summarization Techniques." In Mining Text Data. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-3223-4_3.

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Akinkunmi, Mustapha. "Quantitative Techniques." In Data Mining and Market Intelligence. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-031-79390-5_4.

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Akinkunmi, Mustapha. "Qualitative Techniques." In Data Mining and Market Intelligence. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-031-79390-5_3.

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Conference papers on the topic "Data Mining Techniques"

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Han, Jiawei. "Data mining techniques." In the 1996 ACM SIGMOD international conference. ACM Press, 1996. http://dx.doi.org/10.1145/233269.280351.

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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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Gago, M., and E. Juaristi. "Can thewavelet-kernelmethodology improve other kernel techniques?" In DATA MINING 2008. WIT Press, 2008. http://dx.doi.org/10.2495/data080081.

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Mattas, Nisha, Smarika, and Deepti Mehrotra. "Comparing Data Mining Techniques for Mining Patents." In 2015 Fifth International Conference on Advanced Computing & Communication Technologies (ACCT). IEEE, 2015. http://dx.doi.org/10.1109/acct.2015.119.

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Senkamalavalli, R., and T. Bhuvaneshwari. "Data mining techniques for CRM." In 2014 International Conference on Information Communication and Embedded Systems (ICICES). IEEE, 2014. http://dx.doi.org/10.1109/icices.2014.7033776.

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Khan, Wajid, Fiaz Hussain, and Edmond C. Prakash. "Intelligent Techniques for Data Mining." In Annual International Conference on Computer Games, Multimedia and Allied Technology. Global Science & Technology Forum (GSTF), 2015. http://dx.doi.org/10.5176/2251-1679_cgat15.51.

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Hong, Tzung-Pei, Chun-Hao Chen, and Vincent S. Tseng. "Genetic-Fuzzy Data Mining Techniques." In 2010 IEEE International Conference on Granular Computing (GrC-2010). IEEE, 2010. http://dx.doi.org/10.1109/grc.2010.157.

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Baralis, Elena, Luca Cagliero, Tania Cerquitelli, et al. "NEMICO: Mining Network Data through Cloud-Based Data Mining Techniques." In 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing (UCC). IEEE, 2014. http://dx.doi.org/10.1109/ucc.2014.72.

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Grytsenko, T., and A. Peratta. "Performance assessment of parallel techniques." In DATA MINING & INFORMATION ENGINEERING 2007. WIT Press, 2007. http://dx.doi.org/10.2495/data070091.

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Horta, R. A. M., H. M. Pires, and B. S. L. P. de Lima. "Application of data mining techniques for understanding capital structure of Brazilian companies." In DATA MINING 2009. WIT Press, 2009. http://dx.doi.org/10.2495/data090111.

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Reports on the topic "Data Mining Techniques"

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Wegman, Edward J., and Don R. Faxon. Intrusion Detection Using Data Mining Techniques. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada421061.

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Jajodia, Sushi. Integration of Audit Data Analysis and Mining Techniques into Aide. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada456840.

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Walasek, Lukasz. Web Scraping and Data Mining in R. Instats Inc., 2023. http://dx.doi.org/10.61700/ewzzyuw2l3wtw469.

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This 3-day seminar provides a comprehensive understanding of web scraping and data mining techniques using R software. This workshop is essential for PhD students, professors, and professional researchers, offering practical experience, ethical guidance, and troubleshooting strategies for data extraction and analysis, ultimately enhancing research efficiency and innovation. At the end of this course, participants will be competent at scraping web content and working with APIs. An official Instats completion certificate and 2 ECTS Equivalent points are provided at the seminar's conclusion.

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Zinilli, Antonio. Text Mining in Action: Tools and Techniques using Python. Instats Inc., 2024. http://dx.doi.org/10.61700/k4powzm518m5z1739.

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This seminar provides a comprehensive exploration of text mining techniques using Python, tailored for academic researchers seeking to analyze large textual datasets effectively. Participants will gain hands-on experience with Python libraries and methodologies for natural language processing, sentiment analysis, topic modeling, text classification, and more, enhancing their data analysis capabilities across various disciplines.

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Boyer, A., and N. R. Billette. Orebodies and mining environment, links between geology and quantification. Natural Resources Canada/CMSS/Information Management, 1989. http://dx.doi.org/10.4095/331774.

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The application of quantitative techniques to solve geological and geoengineering problems is relatively recent. Often borrowed from other fields, models must be selected with care to ensure that they are appropriate. The introduction, increasing use and rapid development of these techniques follows the fast evolution of more powerful computer hardwares and softwares. Studies have been carried out to establish the usefulness of several quantitative techniques in solving typical geological/geotechnical problems using available exploration and production drillhole data. Results derived from thes

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Trellue, Holly Renee, Michael Lynn Fugate, and Stephen Joesph Tobin. Data Mining Techniques to Estimate Plutonium, Initial Enrichment, Burnup, and Cooling Time in Spent Fuel Assemblies. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1209224.

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Trellue, Holly Renee, Michael Lynn Fugate, and Stephen Joseph Tobin. Data Mining Techniques to Estimate Plutonium, Initial Enrichment, Burnup, and Cooling Time in Spent Fuel Assemblies. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1209318.

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Lagria, Raymond Freth, and Brenda Quismorio. Analyzing Trends in APEC Using Data Analytics. Philippine Institute for Development Studies, 2022. https://doi.org/10.62986/dp2022.04.

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This paper shows that advanced analytics and traditional statistical techniques on available unstructured and structured data can be utilized to understand the themes put forward in APEC's yearly meetings and how member economies have supported these topics through the conduct of APEC projects. The application of text mining algorithms, such as topic modeling on the proceedings of APEC-level annual meetings, namely, APEC Economic Leaders' Meeting (AELM), APEC Annual Ministerial Meeting (AMM), and Senior Officials' Meeting (SOM), generated themes from the text which insight have been discussed.

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Skow, Jason, Dongliang Lu, and Smitha Koduru. PR-244-133731-R01 In-line Inspection Crack Tool Performance Evaluation. Pipeline Research Council International, Inc. (PRCI), 2015. http://dx.doi.org/10.55274/r0010580.

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This report discusses the findings of a detailed analysis and evaluation of the performance capabilities of various in-line inspection (ILI) technologies for managing pipeline crack threats, including crack colonies, isolated cracks and cracks in pipe seam welds. The foundation for the study was the development of an extensive database of crack inspection data collected from pipeline operators through an industry data mining exercise. The data were validated to ensure completeness consistency and accuracy, and then stored in a database. The database was used to characterize the performance of

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Neyedley, K., J. J. Hanley, Z. Zajacz, and M. Fayek. Accessory mineral thermobarometry, trace element chemistry, and stable O isotope systematics, Mooshla Intrusive Complex (MIC), Doyon-Bousquet-LaRonde mining camp, Abitibi greenstone belt, Québec. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/328986.

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The Mooshla Intrusive Complex (MIC) is an Archean polyphase magmatic body located in the Doyon-Bousquet-LaRonde (DBL) mining camp of the Abitibi greenstone belt, Québec, that is spatially associated with numerous gold (Au)-rich VMS, epizonal 'intrusion-related' Au-Cu vein systems, and shear zone-hosted (orogenic?) Au deposits. To elucidate the P-T conditions of crystallization, and oxidation state of the MIC magmas, accessory minerals (zircon, rutile, titanite) have been characterized using a variety of analytical techniques (e.g., trace element thermobarometry). The resulting trace element an

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