Relevant bibliographies by topics / Knowledge Discovery in Databases (KDD)

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Knowledge Discovery in Databases (KDD)'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Knowledge Discovery in Databases (KDD)"

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Piatetsky-Shapiro, Gregory. "Knowledge discovery in databases: Progress report." Knowledge Engineering Review 9, no. 1 (1994): 57–60. http://dx.doi.org/10.1017/s0269888900006573.

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As the number and size of very large databases continues to grow rapidly, so does the need to make sense of them. This need is addressed by the field called knowledge Discovery in Databases (KDD), which combines approaches from machine learning, statistics, intelligent databases, and knowledge acquisition. KDD encompasses a number of different discovery methods, such as clustering, data summarization, learning classification rules, finding dependency networks, analysing changes, and detecting anomalies (Matheus et at., 1993).
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Chen, Po-Chi, Ru-Fang Hsueh, and Shu-Yuen Hwang. "An ILP Based Knowledge Discovery System." International Journal on Artificial Intelligence Tools 06, no. 01 (1997): 63–95. http://dx.doi.org/10.1142/s0218213097000050.

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Interest in research into knowledge discovery in databases (KDD) has been growing continuously because of the rapid increase in the amount of information embedded in real-world data. Several systems have been proposed for studying the KDD process. One main task in a KDD system is to learn important and user-interesting knowledge from a set of collected data. Most proposed systems use simple machine learning methods to learn the pattern. This may result in efficient performance but the discovery quality is less useful. In this paper, we propose a method to integrated a new and complicated machi
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Köster, Frank, and Marco Grawunder. "Eine Anwendung von Knowledge Discovery in Databases im eLearning (An Application of Knowledge Discovery in Databases in eLearning)." i-com 2, no. 2/2003 (2003): 21–28. http://dx.doi.org/10.1524/icom.2.2.21.19590.

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ZusammenfassungDieser Artikel behandelt die Entwicklung eines Assistenzsystems für Nutzer elektronischer Lehr-/Lernmaterialien (eLLM). Dabei wird das simulatorbasierte Pilotentraining als konkrete Beispielanwendung betrachtet. In diesem Kontext wird insbesondere die mögliche Isolation von Nutzern eLLM als Problem hervorgehoben. Arbeiten zu Tutoriellen Systemen, Virtuellen Lerngemeinschaften, Lernarrangements o.Ä. diskutieren ein facettenreiches Instrumentarium zur Behandlung dieses Problems und prägen eben-so unseren Ansatz. Dieser zielt darauf ab, eine tutorielle Unterstützung sowie Aufgaben
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Mishra, Divya, and Ravindra Kumar. "Knowledge Discovery in Databases (KDD): A Comparative Evaluation of Scientific Databases." Asian Journal of Information Science and Technology 7, no. 2 (2017): 28–30. http://dx.doi.org/10.51983/ajist-2017.7.2.154.

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In this information explosion age, a large number of commercial and free online database provided by publishers of information resources is available on web, Libraries of every kind offering various services regarding use of online resources and services to fulfill the information requirements of a large group of users. The present study comparatively analyze the selected databases which aims to serves a scientific community. The Library Science and information personnel all over the world are focusing more and more on development of better, user friendly and affordable discovery solutions to
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Hao, Wu. "On Business-Oriented Knowledge Discovery and Data Mining." Advanced Materials Research 760-762 (September 2013): 2267–71. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.2267.

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This paper will discuss issues in data mining and business processes including Marketing, Finance and Health. In turn, the use of KDD in the complex real-world databases in business and government will push the IT researchers to identify and solve cutting-edge problems in KDD modelling, techniques and processes. From IT perspectives, some issues in economic sciences consist of business modelling and mining, aberrant behavior detection, and health economics. Some issues in KDD include data mining for complex data structures and complex modelling. These novel strategies will be integrated to bui
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Głowania, Szymon, Jan Kozak, and Przemysław Juszczuk. "Knowledge Discovery in Databases for a Football Match Result." Electronics 12, no. 12 (2023): 2712. http://dx.doi.org/10.3390/electronics12122712.

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The analysis of sports data and the possibility of using machine learning in the prediction of sports results is an increasingly popular topic of research and application. The main problem, apart from choosing the right algorithm, is to obtain data that allow for effective prediction. The article presents a comprehensive KDD (Knowledge Discovery in Databases) approach that allows for the appropriate preparation of data for sports prediction on sports data. The first part of the article covers the subject of KDD and sports data. The next section presents an approach to developing a dataset on t
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Jahani, Alireza, Peyman Akhavan, Mostafa Jafari, and Mohammad Fathian. "Conceptual model for knowledge discovery process in databases based on multi-agent system." VINE Journal of Information and Knowledge Management Systems 46, no. 2 (2016): 207–31. http://dx.doi.org/10.1108/vjikms-01-2015-0003.

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Purpose Knowledge discovery in databases (KDD) is a tedious and repetitive process. A challenge for the effective use of KDD is understanding and confirming its results derived from the harmonized process. To exploit the advantages of agents’ application, this paper aims to propose a conceptual model based on a multi-agent system (MAS) to control each step of the KDD process. Design/methodology/approach This paper reports the empirical findings of a survey conducted among academic and industrial sectors in Tehran, Iran. In this survey, the participants answered a questionnaire about the main f
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Qin, Yuan Bo, and Dong Xin Lu. "The Application of KDD in HIS." Applied Mechanics and Materials 263-266 (December 2012): 1510–14. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.1510.

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This paper summarizes the concepts of AI(artificial intelligence), ML(machine learning) and KDD (knowledge discovery in databases), including the development, definition, 5 knowledge types, 7 tasks, processes and technologies of KDD. It also introduces the HIS(hospital information system), including the introduction and benefits and HIS in China. At last the paper illustrates the application of KDD in HIS, especially in detecting and explaining key information from databases.
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Storti, Edoardo, Laura Cattaneo, Adalberto Polenghi, and Luca Fumagalli. "Customized Knowledge Discovery in Databases methodology for the Control of Assembly Systems." Machines 6, no. 4 (2018): 45. http://dx.doi.org/10.3390/machines6040045.

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The advent of Industry 4.0 has brought to extremely powerful data collection possibilities. Despite this, the potential contained in databases is often partially exploited, especially focusing on the manufacturing field. There are several root causes of this paradox, but the crucial one is the absence of a well-established and standardized Industrial Big Data Analytics procedure, in particular for the application within the assembly systems. This work aims to develop a customized Knowledge Discovery in Databases (KDD) procedure for its application within the assembly department of Bosch VHIT S
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Singgalen, Yerik Afrianto. "Utilizing Knowledge Discovery in Databases (KDD) for Hotel Guest Feedback Analysis." Journal of Computer System and Informatics (JoSYC) 6, no. 1 (2024): 117–32. https://doi.org/10.47065/josyc.v6i1.6094.

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This research explores the application of Knowledge Discovery in Databases (KDD) to analyze hotel guest feedback and improve service quality at Bintang Flores Hotel in Labuan Bajo. Utilizing KDD methodologies, the study processed 589 guest reviews to identify key factors influencing customer satisfaction, including cleanliness (1.00), location (0.82), and staff service (0.71). The analysis also highlighted issues such as limited breakfast variety (0.59) and inconsistent Wi-Fi connectivity (0.41) as recurring concerns, especially for long-term guests and business travelers. The data revealed th
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Dissertations / Theses on the topic "Knowledge Discovery in Databases (KDD)"

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Storti, Emanuele. "KDD process design in collaborative and distributed environments." Doctoral thesis, Università Politecnica delle Marche, 2012. http://hdl.handle.net/11566/242061.

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Il termine Knowledge Discovery in Databases (KDD) si riferisce al processo di scoperta di conoscenza all'interno di grandi volumi di dati, per mezzo di specifici algoritmi. L'applicazione di tali tecniche a contesti organizzativi reali risulta oggi ancora limitata, principalmente a causa della complessità nella configurazione degli algoritmi di analisi dei dati e nella difficoltà nella gestione/esecuzione dei processi di KDD, che impone spesso di far riferimento a contesti di computazione distribuita ed alla interazione tra diversi utenti, tra i quali specialisti con competenze tecniche ed esp
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Huynh, Xuan-Hiep. "Interestingness Measures for Association Rules in a KDD Process : PostProcessing of Rules with ARQAT Tool." Phd thesis, Université de Nantes, 2006. http://tel.archives-ouvertes.fr/tel-00482649.

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This work takes place in the framework of Knowledge Discovery in Databases (KDD), often called "Data Mining". This domain is both a main research topic and an application ¯eld in companies. KDD aims at discovering previously unknown and useful knowledge in large databases. In the last decade many researches have been published about association rules, which are frequently used in data mining. Association rules, which are implicative tendencies in data, have the advantage to be an unsupervised model. But, in counter part, they often deliver a large number of rules. As a consequence, a postproce
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Ribeiro, Lamark dos Santos. "Uma abordagem semântica para seleção de atributos no processo de KDD." Universidade Federal da Paraí­ba, 2010. http://tede.biblioteca.ufpb.br:8080/handle/tede/6048.

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Made available in DSpace on 2015-05-14T12:36:27Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 2925122 bytes, checksum: e65ad4a8f7ca12fb8a90eaf2a8783d65 (MD5) Previous issue date: 2010-08-27<br>Coordenação de Aperfeiçoamento de Pessoal de Nível Superior<br>Currently, two issues of great importance for the computation are being used together in an increasingly apparent: a Knowledge Discovery in Databases (KDD) and Ontologies. By developing the ways in which data is stored, the amount of information available for analysis has increased exponentially, making it necessary techniques to analyze d
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Storopoli, José Eduardo. "O uso do Knowledge Discovery in Database (KDD) de informações patentárias sobre ensino a distância: contribuições para instituições de ensino superior." Universidade Nove de Julho, 2016. http://bibliotecatede.uninove.br/handle/tede/1517.

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Submitted by Nadir Basilio (nadirsb@uninove.br) on 2016-09-01T19:37:54Z No. of bitstreams: 1 José Eduardo Storopoli.pdf: 3248722 bytes, checksum: c6f49ec5728d3ca3b10f36aa03c94865 (MD5)<br>Made available in DSpace on 2016-09-01T19:37:54Z (GMT). No. of bitstreams: 1 José Eduardo Storopoli.pdf: 3248722 bytes, checksum: c6f49ec5728d3ca3b10f36aa03c94865 (MD5) Previous issue date: 2016-04-14<br>Distance learning (DL) has a long history of success and failures, and has existed for at least since the end of the XVIII century. Higher education DL began in Brazil during 1994, having the expansion
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Oliveira, Robson Butaca Taborelli de. "O processo de extração de conhecimento de base de dados apoiado por agentes de software." Universidade de São Paulo, 2000. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-23092001-231242/.

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Os sistemas de aplicações científicas e comerciais geram, cada vez mais, imensas quantidades de dados os quais dificilmente podem ser analisados sem que sejam usados técnicas e ferramentas adequadas de análise. Além disso, muitas destas aplicações são voltadas para Internet, ou seja, possuem seus dados distribuídos, o que dificulta ainda mais a realização de tarefas como a coleta de dados. A área de Extração de Conhecimento de Base de Dados diz respeito às técnicas e ferramentas usadas para descobrir automaticamente conhecimento embutido nos dados. Num ambiente de rede de computadores, é mais
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Scarinci, Rui Gureghian. "SES : sistema de extração semântica de informações." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 1997. http://hdl.handle.net/10183/18398.

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Entre as áreas que mais se desenvolvem na informática nos últimos anos estão aquelas relacionadas ao crescimento da rede Internet, que interliga milhões de usuários de todo o mundo. Esta rede disponibiliza aos usuários uma a enorme variedade e quantidade de informações, principalmente dados armazenados de forma não estruturada ou semi estruturada. Contudo, tal volume e heterogeneidade acaba dificultando a manipulação dos dados recuperados a partir da Internet. Este problema motivou o desenvolvimento deste trabalho. Mesmo com o auxílio de várias ferramentas de pesquisa na Internet, buscando rea
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Moretti, Caio Benatti. "Análise de grandezas cinemáticas e dinâmicas inerentes à hemiparesia através da descoberta de conhecimento em bases de dados." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/18/18149/tde-13062016-184240/.

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Em virtude de uma elevada expectativa de vida mundial, faz-se crescente a probabilidade de ocorrer acidentes naturais e traumas físicos no cotidiano, o que ocasiona um aumento na demanda por reabilitação. A terapia física, sob o paradigma da reabilitação robótica com serious games, oferece maior motivação e engajamento do paciente ao tratamento, cujo emprego foi recomendado pela American Heart Association (AHA), apontando a mais alta avaliação (Level A) para pacientes internados e ambulatoriais. No entanto, o potencial de análise dos dados coletados pelos dispositivos robóticos envolvidos é po
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Schneider, Luís Felipe. "Aplicação do processo de descoberta de conhecimento em dados do poder judiciário do estado do Rio Grande do Sul." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2003. http://hdl.handle.net/10183/8968.

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Para explorar as relações existentes entre os dados abriu-se espaço para a procura de conhecimento e informações úteis não conhecidas, a partir de grandes conjuntos de dados armazenados. A este campo deu-se o nome de Descoberta de Conhecimento em Base de Dados (DCBD), o qual foi formalizado em 1989. O DCBD é composto por um processo de etapas ou fases, de natureza iterativa e interativa. Este trabalho baseou-se na metodologia CRISP-DM . Independente da metodologia empregada, este processo tem uma fase que pode ser considerada o núcleo da DCBD, a “mineração de dados” (ou modelagem conforme CRIS
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Yu, Xiaobo. "Knowledge discovery in Internet databases." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/mq30577.pdf.

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Howard, Craig M. "Tools and techniques for knowledge discovery." Thesis, University of East Anglia, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.368357.

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Books on the topic "Knowledge Discovery in Databases (KDD)"

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Hongjun, Lu, Motoda Hiroshi, and Liu Huan 1958-, eds. KDD, techniques and applications: Proceedings of the First Pacific-Asia Conference on Knowledge Discovery and Data Mining, 23-24 Feb. 97. World Scientific, 1997.

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International Conference on Knowledge Discovery & Data Mining (9th 2003 Washington, D.C.). KDD-2003: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 24-27, 2003, Washington, DC, USA. Association for Computing Machinery, 2003.

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International Conference on Knowledge Discovery & Data Mining (10th 2004 Seattle, Wash.). KDD-2004: Proceedings of the tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining : August 22-25, 2004, Seattle, Washington, USA. ACM Press, 2004.

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International Conference on Knowledge Discovery & Data Mining (9th 2003 Washington, D.C.). KDD-2003: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 24-27, 2003, Washington, DC, USA. Association for Computing Machinery, 2003.

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International Conference on Knowledge Discovery & Data Mining (11th 2005 Chicago, Ill). KDD-2005: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining : August 21-24, 2005, Chicago, Illinois, USA. ACM Press, 2005.

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International Conference on Knowledge Discovery & Data Mining (8th 2002 Edmonton, Alta.). KDD-2002: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining : July 23-36, 2002, Edmonton, Alberta, Canada. ACM, 2002.

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International Conference on Knowledge Discovery & Data Mining (7th 2002 San Francisco, Calif.). KDD-2001: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 26-29, 2001, San Francisco, CA, USA. Association for Computing Machinery, 2001.

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ACM SIGKDD International Conference on Knowledge Discovery and Data Mining ((7th 2001 San Francisco, Calif.). KDD-2001: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining : August 26-29, 2001, San Francisco, CA. Association for Computing Machinery, 2001.

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Gaber, Mohamed Medhat. Knowledge Discovery from Sensor Data: Second International Workshop, Sensor-KDD 2008, Las Vegas, NV, USA, August 24-27, 2008, Revised Selected Papers. Springer-Verlag Berlin Heidelberg, 2010.

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Bart, Goethals, and Siebes Arno 1958-, eds. Knowledge discovery in inductive databases: Third International Workshop, KDID 2004, Pisa, Italy, September 20, 2004 : revised selected and invited papers. Springer, 2005.

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Book chapters on the topic "Knowledge Discovery in Databases (KDD)"

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Ruggieri, Salvatore, and Franco Turini. "A KDD Process for Discrimination Discovery." In Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46131-1_28.

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Boulicaut, Jean-François, Mika Klemettinen, and Heikki Mannila. "Modeling KDD Processes within the Inductive Database Framework." In DataWarehousing and Knowledge Discovery. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48298-9_31.

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Grabowski, Hans, Ralf-Stefan Lossack, and Jörg Weißkopf. "Automatic Classification and Creation of Classification Systems Using Methodologies of “Knowledge Discovery in Databases (KDD)”." In Massive Computing. Springer US, 2001. http://dx.doi.org/10.1007/978-1-4757-4911-3_5.

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Panov, Panče, Larisa Soldatova, and Sašo Džeroski. "OntoDM-KDD: Ontology for Representing the Knowledge Discovery Process." In Discovery Science. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40897-7_9.

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Moczulski, Wojciech, Robert Szulim, Piotr Tomasik, and Dominik Wachla. "Knowledge Discovery in Databases." In Modeling, Diagnostics and Process Control. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16653-2_4.

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Sharafi, Armin. "Knowledge Discovery in Databases." In Knowledge Discovery in Databases. Springer Fachmedien Wiesbaden, 2013. http://dx.doi.org/10.1007/978-3-658-02002-6_3.

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Beekmann, Frank. "Knowledge Discovery in Databases." In Stichprobenbasierte Assoziationsanalyse im Rahmen des Knowledge Discovery in Databases. Deutscher Universitätsverlag, 2003. http://dx.doi.org/10.1007/978-3-322-81227-8_2.

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Klösgen, Willi, and Jan Zytkow. "Techniques and applications of KDD." In Principles of Data Mining and Knowledge Discovery. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63223-9_140.

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Freitas, Alex A., and Simon H. Lavington. "Knowledge Discovery Tasks." In Mining Very Large Databases with Parallel Processing. Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-5521-6_2.

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Freitas, Alex A., and Simon H. Lavington. "Knowledge Discovery Paradigms." In Mining Very Large Databases with Parallel Processing. Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-5521-6_3.

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Conference papers on the topic "Knowledge Discovery in Databases (KDD)"

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Pupezescu, Valentin. "ADVANCES IN KNOWLEDGE DISCOVERY IN DISTRIBUTED DATABASES." In eLSE 2015. Carol I National Defence University Publishing House, 2015. http://dx.doi.org/10.12753/2066-026x-15-046.

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The Knowledge Discovery in Distributed Databases is the process of extracting useful information from a collection of data stored in distributed databases. A distributed database is a collection of data replicated over a number of different computers. The best suited structures for working with distributed databases are the Distributed Committee-Machines. Distributed Committee-Machines are a combination of neural networks that work in a distributed manner as a group in order to obtain better performance than individual neural networks in solving data mining tasks inside the KDD process. In thi
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Lorena, Ana C., Filipe A. N. Verri, and Tiago A. Almeida. "The 5th Brazilian Competition on Knowledge Discovery in Databases (KDD-BR 2021)." In Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/eniac.2021.18425.

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Este artigo editorial descreve a Competição Brasileira de Descoberta de Conhecimento em Bancos de Dados (KDD-BR 2021) e resume as contribuições das três melhores soluções obtidas em sua quinta edição. A competição de 2021 envolveu a resolução de instâncias do Problema do Caixeiro Viajante, de diferentes tamanhos, usando uma abordagem de previsão de arestas.
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Elezi, Fatos, Armin Sharafi, Alexander Mirson, Petra Wolf, Helmut Krcmar, and Udo Lindemann. "A Knowledge Discovery in Databases (KDD) Approach for Extracting Causes of Iterations in Engineering Change Orders." In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-48335.

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This paper describes an implementation of a Knowledge Discovery in Databases (KDD) process for extracting the causes of iterations in Engineering Change Orders (ECOs). A data set of approximately 53,000 historical Engineering Change Orders (ECOs) was used for this purpose. Initially, the impact of iterations in ECO lead time and uncertainty is assessed. Subsequently, a semi-automatic text-mining process is employed to classify the causes of iterations. As a result, cost and technical categories of causes were identified as the main reasons for the occurrence of iterations. The study concludes
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Hu, Qi, Haoran Li, Jiaxin Bai, Zihao Wang, and Yangqiu Song. "Privacy-Preserved Neural Graph Databases." In KDD '24: The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 2024. http://dx.doi.org/10.1145/3637528.3671678.

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Lorena, A. C., D. S. Kaster, R. Cerri, E. R. Faria, and V. V. de Melo. "Can I make a wish?: a competition on detecting meteors in images." In Symposium on Knowledge Discovery, Mining and Learning. Sociedade Brasileira de Computação - SBC, 2018. http://dx.doi.org/10.5753/kdmile.2018.27389.

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Promoting competitions has become a path towards attracting people’s interest into diverse areas. Many international conferences have sessions dedicated to one or more competitions, in which participants are challenged by real problems for which advanced solutions are needed. This paper describes the first Brazilian competition on Knowledge Discovery in Databases (KDD-BR), which was part of three main events of the Brazilian Computer Society dedicated to Artificial Intelligence, Databases and Data Mining. In this first edition the participants were supposed to detect meteors, popularly known a
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Ben Ahmed, Walid, Michel Bigand, Mounib Mekhilef, and Yves Page. "Development of Knowledge Based System to Facilitate Design of On-Board Car Safety Systems." In ASME 2003 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/detc2003/dac-48743.

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The development of on-board car safety systems requires an accidentology knowledge base for the development of new functionalities as well as their improvement and evaluation. The Knowledge Discovery in accident Database (KDD) is one of the approaches allowing the construction of this knowledge base. However, considering the complexity of the accident data and the variety of their sources (biomechanics, psychology, mechanics, ergonomics, etc.), the analytical methods of the KDD (clustering, classification, association rules etc.) should be combined with expert approaches. Indeed, there is back
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Qin, Zongyue, Yunsheng Bai, and Yizhou Sun. "GHashing: Semantic Graph Hashing for Approximate Similarity Search in Graph Databases." In KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 2020. http://dx.doi.org/10.1145/3394486.3403257.

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Comendador, Benilda Eleonor V., Lorena W. Rabago, and Bartolome T. Tanguilig. "An educational model based on Knowledge Discovery in Databases (KDD) to predict learner's behavior using classification techniques." In 2016 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC). IEEE, 2016. http://dx.doi.org/10.1109/icspcc.2016.7753623.

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Stranieri, Andrew, John Yearwood, and Binh Pham. "Combining knowledge discovery from databases (KDD) and case-based reasoning (CBR) to support diagnosis of medical images." In Research Workshop on Automated Medical Image Analysis, edited by Binh Pham, Michael Braun, Anthony J. Maeder, and Michael P. Eckert. SPIE, 1999. http://dx.doi.org/10.1117/12.351621.

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Abdulahi Hasan, Abdulkadir, and Huan Fang. "Data Mining in Education: Discussing Knowledge Discovery in Database (KDD) with Cluster Associative Study." In ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information Systems. ACM, 2021. http://dx.doi.org/10.1145/3469213.3471319.

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Reports on the topic "Knowledge Discovery in Databases (KDD)"

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List, Markus, Quirin Manz, Judith Bernett, et al. D2.1 Whitepaper on the platform knowledge base and data standards for in silico drug repurposing. REPO4EU, 2024. https://doi.org/10.58647/repo4eu.202400d2.1.

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Computational drug repurposing integrates data from diverse sources, such as sequence databases, GWAS studies, or high-throughput screens. Depending on the original use case or field of research, they vary in availability, timeliness, and compatibility with other data sources. Further, numerous computational tools have been introduced designed to identify active disease modules, indications, or drug-target interactions that use different methods and strategies while not adhering to standard guidelines. Clearing and harmonising the resulting inconsistencies consume essential resources such that
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Saville, Alan, and Caroline Wickham-Jones, eds. Palaeolithic and Mesolithic Scotland : Scottish Archaeological Research Framework Panel Report. Society for Antiquaries of Scotland, 2012. http://dx.doi.org/10.9750/scarf.06.2012.163.

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Why research Palaeolithic and Mesolithic Scotland? Palaeolithic and Mesolithic archaeology sheds light on the first colonisation and subsequent early inhabitation of Scotland. It is a growing and exciting field where increasing Scottish evidence has been given wider significance in the context of European prehistory. It extends over a long period, which saw great changes, including substantial environmental transformations, and the impact of, and societal response to, climate change. The period as a whole provides the foundation for the human occupation of Scotland and is crucial for understan
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