Academic literature on the topic 'Data Mining Techniques'
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Journal articles on the topic "Data Mining Techniques"
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 (October 1, 2011): 137–39. http://dx.doi.org/10.15373/2249555x/may2013/41.
Full textSherdiwala, Kainaz Bomi. "Data Mining Techniques in Stock Market." Indian Journal of Applied Research 4, no. 8 (October 1, 2011): 327–29. http://dx.doi.org/10.15373/2249555x/august2014/82.
Full textS, Gowtham, and Karuppusamy S. "Review of Data Mining Classification Techniques." Bonfring International Journal of Software Engineering and Soft Computing 9, no. 2 (April 30, 2019): 8–11. http://dx.doi.org/10.9756/bijsesc.9013.
Full textM., Inbavalli. "An Intelligent Agent based Mining Techniques for Distributed Data Mining." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (March 31, 2020): 610–17. http://dx.doi.org/10.5373/jardcs/v12sp4/20201527.
Full textSAKURAI, 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.
Full textHegland, Markus. "Data mining techniques." Acta Numerica 10 (May 2001): 313–55. http://dx.doi.org/10.1017/s0962492901000058.
Full textHan, Jiawei. "Data mining techniques." ACM SIGMOD Record 25, no. 2 (June 1996): 545. http://dx.doi.org/10.1145/235968.280351.
Full textD, 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 (June 2019): 165–68. http://dx.doi.org/10.21276/ijcesr.2019.6.6.28.
Full textDelima, Allemar Jhone P. "Predicting Scholarship Grants Using Data Mining Techniques." International Journal of Machine Learning and Computing 9, no. 4 (August 2019): 513–19. http://dx.doi.org/10.18178/ijmlc.2019.9.4.834.
Full textLakshmi R, Aishwarya, and SashiKumar D R. "A Survey of Data Mining Techniques for Internet." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 1 (January 30, 2017): 22–35. http://dx.doi.org/10.23956/ijarcsse/v7i1/0125.
Full textDissertations / Theses on the topic "Data Mining Techniques"
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.
Full textTong, 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.
Full textBurgess, Martin. "Transformation techniques in data mining." Thesis, University of East Anglia, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.410093.
Full textAl-Hashemi, Idrees Yousef. "Applying data mining techniques over big data." Thesis, Boston University, 2013. https://hdl.handle.net/2144/21119.
Full textThe 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 Petabytes stored in the world in 2000. Today’s internet has about 0.1 Zettabytes of data (ZB is about 1021 bytes), and this number will reach 35 ZB by 2020. With such an overwhelming flood of information, present data management systems are not able to scale to this huge amount of raw, unstructured data—in today’s parlance, Big Data. In the present study, we show the basic concepts and design of Big Data tools, algorithms, and techniques. We compare the classical data mining algorithms to the Big Data algorithms by using Hadoop/MapReduce as a core implementation of Big Data for scalable algorithms. We implemented the K-means algorithm and A-priori algorithm with Hadoop/MapReduce on a 5 nodes Hadoop cluster. We explore NoSQL databases for semi-structured, massively large-scaling of data by using MongoDB as an example. Finally, we show the performance between HDFS (Hadoop Distributed File System) and MongoDB data storage for these two algorithms.
Zhou, Wubai. "Data Mining Techniques to Understand Textual Data." FIU Digital Commons, 2017. https://digitalcommons.fiu.edu/etd/3493.
Full textXIAO, XIN. "Data Mining Techniques for Complex User-Generated Data." Doctoral thesis, Politecnico di Torino, 2016. http://hdl.handle.net/11583/2644046.
Full textYu, 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.
Full textCID, 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.
Full textEsta 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 processo de KDD; estudo sobre as técnicas de Rough Sets aplicadas à mineração de dados; análise de ferramentas de mineração de dados do mercado; evolução do projeto Bramining; e a realização de alguns estudos de caso para avaliar o Bramining. O estudo sobre o caso KDD abrangeu todas as suas fases: transformação, limpeza, seleção, mineração de dados e pós-processamento. O resultado obtido serviu de base para o aprimoramento do projeto Bramining. O estudo sobre as técnicas de Rough Sets envolveu a pesquisa de seus conceitos e sua aplicabilidade no contexto de KDD. A teoria de Rough Sets foi apresentada por Zdzislaw Pawlak no início dos anos 80 como uma abordagem matemática para a análise de dados vagos e imprecisos. Este estudo permitiu sua aplicação na ferramenta de mineração de dados desenvolvida. A análise de ferramentas de mineração de dados do mercado abrangeu o estudo e testes de aplicativos baseados em diferentes técnicas, enriquecimento a base de comparação utilizada na avaliação da pesquisa. A evolução do projeto Bramining consistiu no aprimoramento do ambiente KDD desenvolvido em estudos anteriores, passando a incluir técnica de Rough Sets em seu escopo. Os estudos de caso foram conduzidos paralelamente com o uso de Bramining e de outras ferramentas existentes, para efeito de comparação. Os índices apresentados pelo Bramining nos estudos de caso foram considerados, de forma geral, equivalentes aos do software comercial, tendo ambos obtidos regras de boa qualidade na maioria dos casos. O Bramining, entretanto, mostrou-se mais completo para o processo de KDD, graças às diversas opções nele disponíveis para preparação dos dados antes da fase de mineração. Os resultados obtidos comprovaram, através da aplicação desenvolvida, a adequação dos conceitos de Rough Sets à tarefa de classificação de dados. Alguns pontos frágeis da técnica foram identificados, como a necessidade de um mecanismo de apoio para a redução de atributos e a dificuldade em trabalhar com atributos de domínio contínuo. Porém, ao se inserir a técnica em um ambiente mais completo de KDD, como o Bramining, estas deficiências foram sanadas. As opções de preparação da base que o Bramining disponibiliza ao usuário para executar, em particular, a redução e a codificação de atributos permitem deixar os dados em estado adequado à aplicação de Rough Sets. A mineração de dados é uma questão bastante relevante nos dias atuais, e muitos métodos têm sido propostos para as diversas tarefas que dizem respeito a esta questão. A teoria de Rough Sets não mostrou significativas vantagens ou desvantagens em relação a outras técnicas já consagradas, mas foi de grande valia comprovar que há caminhos alternativos para o processo de descoberta de conhecimento.
This dissertation investigates the application of Rough Sets to the process of KDD - Knowledge Discovery in Databases. The main goal of the work was to evaluate the performance of Rough Sets techniques in solving the classification problem. Classification is a task of the Data Mining step in KDD Process that performs the discovery of decision rules that best represent a group of registers in a database. The work had five major steps: study of the KDD process; study of Rough Sets techniques applied to data mining; evaluation of existing data mining tools; development of Bramining project; and execution of some case studies to evaluate Bramining. The study of KDD process included all its steps: transformation, cleaning, selection, data mining and post- processing. The results obtained served as a basis to the enhamcement of Bramining. The study of Rough Sets techniques included the research of theory´s concepts and its applicability at KDD context. The Rough Sets tehory has been introduced by Zdzislaw Pawlak in the early 80´s as a mathematical approach to the analysis of vague and uncertain data. This research made possible the implementation of the technique under the environment of the developed tool. The analysis of existing data mining tools included studying and testing of software based on different techniques, enriching the background used in the evaluation of the research. The evolution of Bramining Project consisted in the enhancement of the KDD environment developed in previous works, including the addition of Rough Sets techniques. The case studies were performed simultaneously with Bramining and a commercial minig tool, for comparison reasons. The quality of the knowledge generated by Bramining was considered equivalent to the results of commercial tool, both providing good decision rules for most of the cases. Nevertheless, Bramining proved to be more adapted to the complete KDD process, thanks to the many available features to prepare data to data mining step. The results achieved through the developed application proved the suitability of Rough Sets concepts to the data classification task. Some weaknesses of the technique were identified, like the need of a previous attribute reduction and the inability to deal with continuous domain data. But as the technique has been inserted in a more complete KDD environment like the Bramining Project, those weaknesses ceased to exist. The features of data preparation available in Bramining environment, particularly the reduction and attribute codification options, enable the user to have the database fairly adapted to the use of Rough Sets algorithms. Data mining is a very relevant issue in present days and many methods have been proposed to the different tasks involved in it. Compared to other techniques, Rough Sets Theory did not bring significant advantages or disadvantages to the process, but it has been of great value to show there are alternate ways to knowledge discovery.
Al-Bataineh, Hussien Suleiman. "Islanding Detection Using Data Mining Techniques." Thesis, North Dakota State University, 2015. https://hdl.handle.net/10365/27634.
Full textJANAKIRAMAN, KRISHNAMOORTHY. "ENTITY IDENTIFICATION USING DATA MINING TECHNIQUES." University of Cincinnati / OhioLINK, 2001. http://rave.ohiolink.edu/etdc/view?acc_num=ucin989852516.
Full textBooks on the topic "Data Mining Techniques"
Berry, Michael J. A. Data Mining Techniques. New York: John Wiley & Sons, Ltd., 2004.
Find full textOlson, David Louis. Advanced Data Mining Techniques. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2008.
Find full textMicheline, Kamber, ed. Data mining: Concepts and techniques. 3rd ed. Burlington, MA: Elsevier, 2011.
Find full textMicheline, Kamber, ed. Data mining: Concepts and techniques. 2nd ed. Amsterdam: Elsevier, 2006.
Find full textMicheline, Kamber, ed. Data mining: Concepts and techniques. New Delhi, India: Elsevier, 2001.
Find full textMicheline, Kamber, ed. Data mining: Concepts and techniques. San Francisco: Morgan Kaufmann Publishers, 2001.
Find full textAppice, Annalisa, Anna Ciampi, Fabio Fumarola, and Donato Malerba. Data Mining Techniques in Sensor Networks. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-5454-9.
Full textData mining: Concepts, models and techniques. Berlin, Heilelberg: Springer-Verlag, 2011.
Find full textShah, Ketan, Neepa Shah, Vinaya Sawant, and Neeraj Parolia. Practical Data Mining Techniques and Applications. Boca Raton: Auerbach Publications, 2023. http://dx.doi.org/10.1201/9781003390220.
Full textCarugo, Oliviero, and Frank Eisenhaber, eds. Data Mining Techniques for the Life Sciences. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2095-3.
Full textBook chapters on the topic "Data Mining Techniques"
Rajola, Federico. "Data Mining Techniques." In Customer Relationship Management, 71–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-24718-0_6.
Full textRamos, 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, 13–30. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22294-3_3.
Full textRajola, Federico. "Data Mining Techniques." In Management for Professionals, 109–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35554-7_8.
Full textThuraisingham, Bhavani, Mohammad Mehedy Masud, Pallabi Parveen, and Latifur Khan. "Data Mining Techniques." In Big Data Analytics with Applications in Insider Threat Detection, 27–42. Boca Raton : Taylor & Francis, CRC Press, 2017.: Auerbach Publications, 2017. http://dx.doi.org/10.1201/9781315119458-4.
Full textLi, Deren, Shuliang Wang, and Deyi Li. "Methods and Techniques in SDM." In Spatial Data Mining, 157–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-48538-5_5.
Full textBoire, Richard. "Applying Data Mining Techniques." In Data Mining for Managers, 95–113. New York: Palgrave Macmillan US, 2014. http://dx.doi.org/10.1057/9781137406194_12.
Full textNenkova, Ani, and Kathleen McKeown. "A Survey of Text Summarization Techniques." In Mining Text Data, 43–76. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-3223-4_3.
Full textAkinkunmi, Mustapha. "Quantitative Techniques." In Data Mining and Market Intelligence, 19–37. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-031-79390-5_4.
Full textAkinkunmi, Mustapha. "Qualitative Techniques." In Data Mining and Market Intelligence, 15–17. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-031-79390-5_3.
Full textTeh, Ying Wah, and Abu Bakar Zaitun. "Data Mining Techniques in Index Techniques." In Intelligent Systems Design and Applications, 331–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-44999-7_32.
Full textConference papers on the topic "Data Mining Techniques"
Han, Jiawei. "Data mining techniques." In the 1996 ACM SIGMOD international conference. New York, New York, USA: ACM Press, 1996. http://dx.doi.org/10.1145/233269.280351.
Full textAgarwal, 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.
Full textGago, M., and E. Juaristi. "Can thewavelet-kernelmethodology improve other kernel techniques?" In DATA MINING 2008. Southampton, UK: WIT Press, 2008. http://dx.doi.org/10.2495/data080081.
Full textMattas, 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.
Full textSenkamalavalli, 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.
Full textKhan, 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.
Full textHong, 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.
Full textGrytsenko, T., and A. Peratta. "Performance assessment of parallel techniques." In DATA MINING & INFORMATION ENGINEERING 2007. Southampton, UK: WIT Press, 2007. http://dx.doi.org/10.2495/data070091.
Full textBaralis, Elena, Luca Cagliero, Tania Cerquitelli, Silvia Chiusano, Paolo Garza, Luigi Grimaudo, and Fabio Pulvirenti. "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.
Full textHorta, 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. Southampton, UK: WIT Press, 2009. http://dx.doi.org/10.2495/data090111.
Full textReports on the topic "Data Mining Techniques"
Wegman, Edward J., and Don R. Faxon. Intrusion Detection Using Data Mining Techniques. Fort Belvoir, VA: Defense Technical Information Center, January 2004. http://dx.doi.org/10.21236/ada421061.
Full textJajodia, Sushi. Integration of Audit Data Analysis and Mining Techniques into Aide. Fort Belvoir, VA: Defense Technical Information Center, July 2006. http://dx.doi.org/10.21236/ada456840.
Full textWalasek, Lukasz. Web Scraping and Data Mining in R. Instats Inc., 2023. http://dx.doi.org/10.61700/ewzzyuw2l3wtw469.
Full textTrellue, 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), March 2015. http://dx.doi.org/10.2172/1209224.
Full textTrellue, 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), March 2015. http://dx.doi.org/10.2172/1209318.
Full textBoyer, 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.
Full textNeyedley, 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.
Full textSkow, Jason, Dongliang Lu, and Smitha Koduru. PR-244-133731-R01 In-line Inspection Crack Tool Performance Evaluation. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), July 2015. http://dx.doi.org/10.55274/r0010580.
Full textNeyedley, K., J. J. Hanley, P. Mercier-Langevin, and M. Fayek. Ore mineralogy, pyrite chemistry, and S isotope systematics of magmatic-hydrothermal Au mineralization associated with the Mooshla Intrusive Complex (MIC), Doyon-Bousquet-LaRonde mining camp, Abitibi greenstone belt, Québec. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/328985.
Full textKong, Zhihao, and Na Lu. Determining Optimal Traffic Opening Time Through Concrete Strength Monitoring: Wireless Sensing. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317613.
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