Academic literature on the topic 'Data Mining and Knowledge DiscoveryID'
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Journal articles on the topic "Data Mining and Knowledge DiscoveryID"
Bhojani, Shital Hitesh. "Geospatial Data Mining Techniques: Knowledge Discovery in Agricultural." Indian Journal of Applied Research 3, no. 1 (October 1, 2011): 22–24. http://dx.doi.org/10.15373/2249555x/jan2013/10.
Full textAbdulkadium, Ahmed Mahdi, Raid Abd Alreda Shekan, and Haitham Ali Hussain. "Application of Data Mining and Knowledge Discovery in Medical Databases." Webology 19, no. 1 (January 20, 2022): 4912–24. http://dx.doi.org/10.14704/web/v19i1/web19329.
Full textKAWANO, Hiroyuki. "Knowledge Discovery and Data Mining." Journal of Japan Society for Fuzzy Theory and Systems 9, no. 6 (1997): 851–60. http://dx.doi.org/10.3156/jfuzzy.9.6_851.
Full textIWASAKI, Manabu. "Data Mining and Knowledge Discovery." Kodo Keiryogaku (The Japanese Journal of Behaviormetrics) 26, no. 1 (1999): 46–58. http://dx.doi.org/10.2333/jbhmk.26.46.
Full textChikalkar, Siddharth Nandakumar. "Knowledge Discovery and Data Mining." International Journal for Research in Applied Science and Engineering Technology 8, no. 10 (October 31, 2020): 874–76. http://dx.doi.org/10.22214/ijraset.2020.32045.
Full textBrodley, Carla, Terran Lane, and Timothy Stough. "Knowledge Discovery and Data Mining." American Scientist 87, no. 1 (1999): 54. http://dx.doi.org/10.1511/1999.16.807.
Full textLee, Hing-Yan, Hongjun Lu, and Hiroshi Motoda. "Knowledge discovery and data mining." Knowledge-Based Systems 10, no. 7 (May 1998): 401–2. http://dx.doi.org/10.1016/s0950-7051(98)00033-1.
Full textClancy, Thomas Roy, and Lillee Gelinas. "Knowledge Discovery and Data Mining." JONA: The Journal of Nursing Administration 46, no. 9 (September 2016): 422–24. http://dx.doi.org/10.1097/nna.0000000000000369.
Full textRuzgas, Tomas, Kristina Jakubėlienė, and Aistė Buivytė. "Big Data Mining and Knowledge Discovery." Journal of Communications Technology, Electronics and Computer Science 9 (December 27, 2016): 5. http://dx.doi.org/10.22385/jctecs.v9i0.134.
Full textGupta, Aman. "Data Mining to Discovery of Knowledge." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 309–11. http://dx.doi.org/10.22214/ijraset.2022.43643.
Full textDissertations / Theses on the topic "Data Mining and Knowledge DiscoveryID"
Amado, Vanessa. "Knowledge discovery and data mining from freeway section traffic data." Diss., Columbia, Mo. : University of Missouri-Columbia, 2008. http://hdl.handle.net/10355/5591.
Full textThe entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on June 8, 2009) Vita. Includes bibliographical references.
Engels, Robert. "Component based user guidance in knowledge discovery and data mining /." Sankt Augustin : Infix, 1999. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=008752552&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.
Full textPonsan, Christiane. "Computing with words for data mining." Thesis, University of Bristol, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.310744.
Full textAbedjan, Ziawasch. "Improving RDF data with data mining." Phd thesis, Universität Potsdam, 2014. http://opus.kobv.de/ubp/volltexte/2014/7133/.
Full textLinked Open Data (LOD) umfasst viele und oft sehr große öffentlichen Datensätze und Wissensbanken, die hauptsächlich in der RDF Triplestruktur bestehend aus Subjekt, Prädikat und Objekt vorkommen. Dabei repräsentiert jedes Triple einen Fakt. Unglücklicherweise erfordert die Heterogenität der verfügbaren öffentlichen Daten signifikante Integrationsschritte bevor die Daten in Anwendungen genutzt werden können. Meta-Daten wie ontologische Strukturen und Bereichsdefinitionen von Prädikaten sind zwar wünschenswert und idealerweise durch eine Wissensbank verfügbar. Jedoch sind Wissensbanken im Kontext von LOD oft unvollständig oder einfach nicht verfügbar. Deshalb ist es nützlich automatisch Meta-Informationen, wie ontologische Abhängigkeiten, Bereichs-und Domänendefinitionen und thematische Assoziationen von Ressourcen generieren zu können. Eine neue und vielversprechende Technik um solche Daten zu untersuchen basiert auf das entdecken von Assoziationsregeln, welche ursprünglich für Verkaufsanalysen in transaktionalen Datenbanken angewendet wurde. Wir haben eine Adaptierung dieser Technik auf RDF Daten entworfen und stellen das Konzept der Mining Konfigurationen vor, welches uns befähigt in RDF Daten auf unterschiedlichen Weisen Muster zu erkennen. Verschiedene Konfigurationen erlauben uns Schema- und Wertbeziehungen zu erkennen, die für interessante Anwendungen genutzt werden können. In dem Sinne, stellen wir assoziationsbasierte Verfahren für eine Prädikatvorschlagsverfahren, Datenvervollständigung, Ontologieverbesserung und Anfrageerleichterung vor. Das Vorschlagen von Prädikaten behandelt das Problem der inkonsistenten Verwendung von Ontologien, indem einem Benutzer, der einen neuen Fakt einem Rdf-Datensatz hinzufügen will, eine sortierte Liste von passenden Prädikaten vorgeschlagen wird. Eine Kombinierung von verschiedenen Konfigurationen erweitert dieses Verfahren sodass automatisch komplett neue Fakten für eine Wissensbank generiert werden. Hierbei stellen wir zwei Verfahren vor, einen nutzergesteuertenVerfahren, bei dem ein Nutzer die Entität aussucht die erweitert werden soll und einen datengesteuerten Ansatz, bei dem ein Algorithmus selbst die Entitäten aussucht, die mit fehlenden Fakten erweitert werden. Da Wissensbanken stetig wachsen und sich verändern, ist ein anderer Ansatz um die Verwendung von RDF Daten zu erleichtern die Verbesserung von Ontologien. Hierbei präsentieren wir ein Assoziationsregeln-basiertes Verfahren, der Daten und zugrundeliegende Ontologien zusammenführt. Durch die Verflechtung von unterschiedlichen Konfigurationen leiten wir einen neuen Algorithmus her, der gleichbedeutende Prädikate entdeckt. Diese Prädikate können benutzt werden um Ergebnisse einer Anfrage zu erweitern oder einen Nutzer während einer Anfrage zu unterstützen. Für jeden unserer vorgestellten Anwendungen präsentieren wir eine große Auswahl an Experimenten auf Realweltdatensätzen. Die Experimente und Evaluierungen zeigen den Mehrwert von Assoziationsregeln-Generierung für die Integration und Nutzbarkeit von RDF Daten und bestätigen die Angemessenheit unserer konfigurationsbasierten Methodologie um solche Regeln herzuleiten.
PaÌirceÌir, RoÌnaÌn. "Knowledge discovery from distributed aggregate data in data warehouses and statistical databases." Thesis, University of Ulster, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.274398.
Full textSharma, Sumana. "An Integrated Knowledge Discovery and Data Mining Process Model." VCU Scholars Compass, 2008. http://scholarscompass.vcu.edu/etd/1615.
Full textDharaniK and Kalpana Gudikandula. "Actionable Knowledge Discovery using Multi-Step Mining." International Journal of Computer Science and Network (IJCSN), 2012. http://hdl.handle.net/10150/271493.
Full textData mining is a process of obtaining trends or patterns in historical data. Such trends form business intelligence that in turn leads to taking well informed decisions. However, data mining with a single technique does not yield actionable knowledge. This is because enterprises have huge databases and heterogeneous in nature. They also have complex data and mining such data needs multi-step mining instead of single step mining. When multiple approaches are involved, they provide business intelligence in all aspects. That kind of information can lead to actionable knowledge. Recently data mining has got tremendous usage in the real world. The drawback of existing approaches is that insufficient business intelligence in case of huge enterprises. This paper presents the combination of existing works and algorithms. We work on multiple data sources, multiple methods and multiple features. The combined patterns thus obtained from complex business data provide actionable knowledge. A prototype application has been built to test the efficiency of the proposed framework which combines multiple data sources, multiple methods and multiple features in mining process. The empirical results revealed that the proposed approach is effective and can be used in the real world.
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 textButler, Patrick Julian Carey. "Knowledge Discovery in Intelligence Analysis." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/48422.
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Atzmüller, 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 textBooks on the topic "Data Mining and Knowledge DiscoveryID"
Tan, Honghua, ed. Knowledge Discovery and Data Mining. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27708-5.
Full textMaimon, Oded, and Mark Last. Knowledge Discovery and Data Mining. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4757-3296-2.
Full textTan, Honghua. Knowledge Discovery and Data Mining. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.
Find full text1948-, Bramer M. A., ed. Knowledge discovery and data mining. London: The Institution of Electrical Engineers, 1999.
Find full textDavid, Taniar, ed. Data mining and knowledge discovery technologies. Hershey: IGI Pub., 2008.
Find full textZ, Maimon Oded, and Rokach Lior, eds. Data mining and knowledge discovery handbook. New York: Springer, 2005.
Find full textJ, Miller Harvey, and Han Jiawei, eds. Geographic data mining and knowledge discovery. London: Taylor & Francis, 2001.
Find full textPetrushin, Valery A., and Latifur Khan, eds. Multimedia Data Mining and Knowledge Discovery. London: Springer London, 2007. http://dx.doi.org/10.1007/978-1-84628-799-2.
Full textCios, Krzysztof J., Witold Pedrycz, and Roman W. Swiniarski. Data Mining Methods for Knowledge Discovery. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5589-6.
Full textGaber, Mohamed Medhat, ed. Scientific Data Mining and Knowledge Discovery. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-02788-8.
Full textBook chapters on the topic "Data Mining and Knowledge DiscoveryID"
Kumar, Hanuma, Rohit Paravastu, and Vikram Pudi. "Specialty Mining." In Data Warehousing and Knowledge Discovery, 227–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15105-7_18.
Full textFayyad, Usama. "Knowledge Discovery in Databases: An Overview." In Relational Data Mining, 28–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04599-2_2.
Full textGaber, Mohamed Medhat, Arkady Zaslavsky, and Shonali Krishnaswamy. "Data Stream Mining." In Data Mining and Knowledge Discovery Handbook, 759–87. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-09823-4_39.
Full textShekhar, Shashi, Pusheng Zhang, and Yan Huang. "Spatial Data Mining." In Data Mining and Knowledge Discovery Handbook, 837–54. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-09823-4_43.
Full textDžeroski, Sašo. "Relational Data Mining." In Data Mining and Knowledge Discovery Handbook, 887–911. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-09823-4_46.
Full textMoyle, Steve. "Collaborative Data Mining." In Data Mining and Knowledge Discovery Handbook, 1029–39. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-09823-4_54.
Full textNemati, Hamid R., and Christopher D. Barko. "Organizational Data Mining." In Data Mining and Knowledge Discovery Handbook, 1041–48. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-09823-4_55.
Full textZhang, Zhongfei (Mark), and Ruofei Zhang. "Multimedia Data Mining." In Data Mining and Knowledge Discovery Handbook, 1081–109. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-09823-4_57.
Full textPiatetsky-Shapiro, Gregory. "The Journey of Knowledge Discovery." In Journeys to Data Mining, 173–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28047-4_13.
Full textMaimon, Oded, and Mark Last. "Advanced data mining methods." In Knowledge Discovery and Data Mining, 123–33. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4757-3296-2_8.
Full textConference papers on the topic "Data Mining and Knowledge DiscoveryID"
Caputo, G. M., V. M. Bastos, A. M. Cister, and N. F. F. Ebecken. "Knowledge discovery for CRM improvement." In DATA MINING 2008. Southampton, UK: WIT Press, 2008. http://dx.doi.org/10.2495/data080171.
Full textMilne, R. "Knowledge guided data mining." In IEE Colloquium on Knowledge Discovery in Databases. IEE, 1995. http://dx.doi.org/10.1049/ic:19950117.
Full textPeyton, L., and J. Hu. "Knowledge discovery in a circle of trust." In DATA MINING & INFORMATION ENGINEERING 2007. Southampton, UK: WIT Press, 2007. http://dx.doi.org/10.2495/data070221.
Full textLuo, Qi. "Advancing Knowledge Discovery and Data Mining." In First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008). IEEE, 2008. http://dx.doi.org/10.1109/wkdd.2008.153.
Full textKhabaza, T. "Data mining with Clementine." In IEE Colloquium on Knowledge Discovery in Databases. IEE, 1995. http://dx.doi.org/10.1049/ic:19950121.
Full textScarfe, R. T. "Data mining applications in BT." In IEE Colloquium on Knowledge Discovery in Databases. IEE, 1995. http://dx.doi.org/10.1049/ic:19950125.
Full textHoreis, Timo, and Bernhard Sick. "Collaborative Knowledge Discovery & Data Mining: From Knowledge to Experience." In 2007 IEEE Symposium on Computational Intelligence and Data Mining. IEEE, 2007. http://dx.doi.org/10.1109/cidm.2007.368905.
Full textKecahdi, M.-Tahar, and Ilias K. Savvas. "Cooperative Knowledge Discovery & Data Mining CKDD." In 2010 19th IEEE International Workshops on Enabling Technologies: Infrastructures for Collaborative Enterprises. IEEE, 2010. http://dx.doi.org/10.1109/wetice.2010.21.
Full textGilmore, John F., Michael A. Pagels, and Justin Palk. "Asymmetric threat data mining and knowledge discovery." In Aerospace/Defense Sensing, Simulation, and Controls, edited by Belur V. Dasarathy. SPIE, 2001. http://dx.doi.org/10.1117/12.421076.
Full textJANOSCOVA, RENATA. "COMPUTER AIDED KNOWLEDGE DISCOVERY AND DATA MINING." In QUALITY AND LEADING INNOVATION´2014. Gaudeamus Hradec Kralove, 2014. http://dx.doi.org/10.12776/qali.v1.10.
Full textReports on the topic "Data Mining and Knowledge DiscoveryID"
Phillips, Laurence R., Danyelle N. Jordan, Travis L. Bauer, Mark T. Elmore, Jim N. Treadwell, Rossitza A. Homan, Leon Darrel Chapman, and Shannon V. Spires. Knowledge Discovery and Data Mining (KDDM) survey report. Office of Scientific and Technical Information (OSTI), February 2005. http://dx.doi.org/10.2172/922750.
Full textPou, Jose, Jeff Duffany, and Alfredo Cruz. Terrorist Activity Evaluation and Pattern Detection (TAE&PD) in Afghanistan: A Knowledge Discovery and Data Mining (KDDM) Approach for Counter-Terrorism. Fort Belvoir, VA: Defense Technical Information Center, August 2012. http://dx.doi.org/10.21236/ada581564.
Full textSwaminathan, Subramanyam. A System for Discovering Bioengineered Threats by Knowledge Base Driven Mining of Toxin Data. Fort Belvoir, VA: Defense Technical Information Center, August 2004. http://dx.doi.org/10.21236/ada429799.
Full textBond, W., Maria Seale, and Jeffrey Hensley. A dynamic hyperbolic surface model for responsive data mining. Engineer Research and Development Center (U.S.), April 2022. http://dx.doi.org/10.21079/11681/43886.
Full textVolkova, Nataliia P., Nina O. Rizun, and Maryna V. Nehrey. Data science: opportunities to transform education. [б. в.], September 2019. http://dx.doi.org/10.31812/123456789/3241.
Full textNenci, Silvia, and Francesco Quatraro. Innovation and Competitiveness in Mining Value Chains in Latin America. Inter-American Development Bank, December 2021. http://dx.doi.org/10.18235/0003805.
Full textde Kemp, E. A., H. A. J. Russell, B. Brodaric, D. B. Snyder, M. J. Hillier, M. St-Onge, C. Harrison, et al. Initiating transformative geoscience practice at the Geological Survey of Canada: Canada in 3D. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/331097.
Full textRodriguez Muxica, Natalia. Open configuration options Bioinformatics for Researchers in Life Sciences: Tools and Learning Resources. Inter-American Development Bank, February 2022. http://dx.doi.org/10.18235/0003982.
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