Tesis sobre el tema "Data Mining and Knowledge DiscoveryID"
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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.
Texto completoThe 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.
Texto completoPonsan, Christiane. "Computing with words for data mining". Thesis, University of Bristol, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.310744.
Texto completoAbedjan, Ziawasch. "Improving RDF data with data mining". Phd thesis, Universität Potsdam, 2014. http://opus.kobv.de/ubp/volltexte/2014/7133/.
Texto completoLinked 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.
Texto completoSharma, Sumana. "An Integrated Knowledge Discovery and Data Mining Process Model". VCU Scholars Compass, 2008. http://scholarscompass.vcu.edu/etd/1615.
Texto completoDharaniK y Kalpana Gudikandula. "Actionable Knowledge Discovery using Multi-Step Mining". International Journal of Computer Science and Network (IJCSN), 2012. http://hdl.handle.net/10150/271493.
Texto completoData 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.
Texto completoButler, Patrick Julian Carey. "Knowledge Discovery in Intelligence Analysis". Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/48422.
Texto completoPh. D.
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.
Texto completoBani, Mustafa Ahmed Mahmood. "A knowledge discovery and data mining process model for metabolomics". Thesis, Aberystwyth University, 2012. http://hdl.handle.net/2160/6889468e-851f-47fd-bd44-fe65fe516c7a.
Texto completoHE, AIJING. "UNSUPERVISED DATA MINING BY RECURSIVE PARTITIONING". University of Cincinnati / OhioLINK, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1026406153.
Texto completoHayward, John T. "Mining Oncology Data: Knowledge Discovery in Clinical Performance of Cancer Patients". Worcester, Mass. : Worcester Polytechnic Institute, 2006. http://www.wpi.edu/Pubs/ETD/Available/etd-081606-083026/.
Texto completoKeywords: Clinical Performance; Databases; Cancer; oncology; Knowledge Discovery in Databases; data mining. Includes bibliographical references (leaves 267-270).
Nagao, Katashi, Katsuhiko Kaji y Toshiyuki Shimizu. "Discussion Mining : Knowledge Discovery from Data on the Real World Activities". INTELLIGENT MEDIA INTEGRATION NAGOYA UNIVERSITY / COE, 2004. http://hdl.handle.net/2237/10350.
Texto completoRitchie, J. A. "Knowledge discovery and data mining : operation of the Ireland power system". Thesis, Queen's University Belfast, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.432508.
Texto completoTetley, Michael Grant. "Constraining Earth’s plate tectonic evolution through data mining and knowledge discovery". Thesis, The University of Sydney, 2018. http://hdl.handle.net/2123/18737.
Texto completoFernandez, Sanchez Javier. "Knowledge Discovery and Data Mining Using Demographic and Clinical Data to Diagnose Heart Disease". Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233978.
Texto completoUr-Rahman, Nadeem. "Textual data mining applications for industrial knowledge management solutions". Thesis, Loughborough University, 2010. https://dspace.lboro.ac.uk/2134/6373.
Texto completoMomtazpour, Marjan. "Knowledge Discovery for Sustainable Urban Mobility". Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/65157.
Texto completoPh. D.
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.
Texto completoWu, Qionglin 1964. "Data mining and knowledge discovery in financial research : empirical investigations into currency". Thesis, McGill University, 2001. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=31560.
Texto completoBefore analyzing neural network techniques, data preprocessing and correlation analysis are presented. It is found there exist three correlation situations: the currencies between member countries in European Economic Community (EEC) have very strong correlation relationship; the correlations between Chinese Renminbi and the other currencies are very weak; and the correlations between the other currencies are variable with the change of the time period. They are related to the different finance policy, economic situation and the other factors of each country with the different time period.
Gheyas, Iffat A. "Novel computationally intelligent machine learning algorithms for data mining and knowledge discovery". Thesis, University of Stirling, 2009. http://hdl.handle.net/1893/2152.
Texto completoChen, Xiaodong. "Temporal data mining : algorithms, language and system for temporal association rules". Thesis, Manchester Metropolitan University, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.297977.
Texto completoWu, Sheng-Tang. "Knowledge discovery using pattern taxonomy model in text mining". Queensland University of Technology, 2007. http://eprints.qut.edu.au/16675/.
Texto completoWu, Sheng-Tang. "Knowledge discovery using pattern taxonomy model in text mining". Thesis, Queensland University of Technology, 2007. https://eprints.qut.edu.au/16675/1/Sheng-Tang_Wu_Thesis.pdf.
Texto completoDam, Hai Huong Information Technology & Electrical Engineering Australian Defence Force Academy UNSW. "A scalable evolutionary learning classifier system for knowledge discovery in stream data mining". Awarded by:University of New South Wales - Australian Defence Force Academy, 2008. http://handle.unsw.edu.au/1959.4/38865.
Texto completoCloyd, James Dale. "Data mining with Newton's method". [Johnson City, Tenn. : East Tennessee State University], 2002. http://etd-submit.etsu.edu/etd/theses/available/etd-1101102-081311/unrestricted/CloydJ111302a.pdf.
Texto completoFukuda, Kyoko. "Computer-Enhanced Knowledge Discovery in Environmental Science". Thesis, University of Canterbury. Mathematics and Statistics, 2009. http://hdl.handle.net/10092/2140.
Texto completoBrown, Marvin Lane. "The Impact of Data Imputation Methodologies on Knowledge Discovery". Cleveland State University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=csu1227054769.
Texto completoSun, Xingzhi. "Knowledge discovery in long temporal event sequences /". [St. Lucia, Qld.], 2005. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe18601.pdf.
Texto completoIglesia, Beatriz de la. "The development and application of heuristic techniques for the data mining task of nugget discovery". Thesis, University of East Anglia, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.368386.
Texto completoAmirbekyan, Artak. "Protocols and Data Structures for Knowledge Discovery on Distributed Private Databases". Thesis, Griffith University, 2007. http://hdl.handle.net/10072/367447.
Texto completoThesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Information and Communication Technology
Science, Environment, Engineering and Technology
Full Text
Dopitová, Kateřina. "Empirické porovnání systémů dobývání znalostí z databází". Master's thesis, Vysoká škola ekonomická v Praze, 2010. http://www.nusl.cz/ntk/nusl-18159.
Texto completoFu, Tianjun. "CSI in the Web 2.0 Age: Data Collection, Selection, and Investigation for Knowledge Discovery". Diss., The University of Arizona, 2011. http://hdl.handle.net/10150/217073.
Texto completoZhou, Mu. "Knowledge Discovery and Predictive Modeling from Brain Tumor MRIs". Scholar Commons, 2015. http://scholarcommons.usf.edu/etd/5809.
Texto completoChoudhary, Alok K. "Knowledge discovery for moderating collaborative projects". Thesis, Loughborough University, 2009. https://dspace.lboro.ac.uk/2134/8138.
Texto completoKatarina, Gavrić. "Mining large amounts of mobile object data". Phd thesis, Univerzitet u Novom Sadu, Fakultet tehničkih nauka u Novom Sadu, 2017. https://www.cris.uns.ac.rs/record.jsf?recordId=105036&source=NDLTD&language=en.
Texto completoПредмет и циљ истраживања докторске дисертације представља евалуацијамогућности коришћења све веће количине јавно доступних података олокацији и кретању људи, како би се дошло до нових сазнања, развили новимодели понашања и кретања људи који се могу применити за решавањепрактичних проблема као што су: анализа атрактивних туристичких локација,откривање путања кретања људи и средстава транспорта које најчешћекористе, као и откривање важних параметара на основу којих се можеразвити стратегија за заштиту нације од инфективних болести итд. У раду је уту сврхе спроведена практична студија на бази заштићених (агрегираних ианонимизираних) ЦДР података и метаподатака гео-референцираногмултимедијалног садржаја. Приступ је заснован на примени техникавештачке интелигенције и истраживања података.
Predmet i cilj istraživanja doktorske disertacije predstavlja evaluacijamogućnosti korišćenja sve veće količine javno dostupnih podataka olokaciji i kretanju ljudi, kako bi se došlo do novih saznanja, razvili novimodeli ponašanja i kretanja ljudi koji se mogu primeniti za rešavanjepraktičnih problema kao što su: analiza atraktivnih turističkih lokacija,otkrivanje putanja kretanja ljudi i sredstava transporta koje najčešćekoriste, kao i otkrivanje važnih parametara na osnovu kojih se možerazviti strategija za zaštitu nacije od infektivnih bolesti itd. U radu je utu svrhe sprovedena praktična studija na bazi zaštićenih (agregiranih ianonimiziranih) CDR podataka i metapodataka geo-referenciranogmultimedijalnog sadržaja. Pristup je zasnovan na primeni tehnikaveštačke inteligencije i istraživanja podataka.
Raatikainen, M. (Mika). "Intelligent knowledge discovery on building energy and indoor climate data". Doctoral thesis, Oulun yliopisto, 2016. http://urn.fi/urn:isbn:9789526213804.
Texto completoTiivistelmä Tulevaisuuden visio energiansäästön sekä energiatehokkuuden mahdollistavista teknologioista pohjautuu tärkeimpiin tunnistettuihin megatrendeihin, ilmastonmuutokseen, kaupungistumiseen ja digitalisoitumiseen. Yhdysvalloissa ja Euroopan unionissa käytetään noin 40 % kokonaisenergiankulutuksesta rakennusten käytön energiatarpeeseen. Myös rakennusten sisäilmaston on havaittu olevan ilmeinen terveysriski. Perustuen kahteen edellä mainittuun tekijään, energiatehokkuus ja asumisterveys ovat aktiivisia tutkimusaiheita kansainvälisessä tutkimuksessa. Tämän väitöskirjan päätavoitteena on ollut tutkia, mitkä elementit vaikuttavat sisäilmastoon ja rakennusten energiatehokkuuteen pääasiassa analysoimalla mittausdataa käyttäen älykkäitä laskennallisia menetelmiä. Tutkimuksissa käytetyt tiedonkeruuteknologiat perustuvat etäluentaan ja rakennusautomaatioon, big datan hyödyntämiseen ja esineiden internetiin (IoT). Väitöskirjassa esiteltävä tietämyksen muodostusprosessi (KDD) koostuu tiedonkeruusta,datan esikäsittelystä, tiedonlouhinnasta, visualisoinnista ja tutkimustulosten tulkinnasta sekä tietämyksen muodostamisesta ja oleellisen informaation esittämisestä loppukäyttäjille. Tässä väitöstutkimuksessa esitellään neljän data-analyysin ja niiden pohjalta muodostetun tietämyksen hyödyntämisen esimerkkiä, jotka liittyvät pientaloihin ja koulurakennuksiin. Esimerkkitapausten tulokset osoittavat, että käytetyillä tiedonlouhinnan menetelmillä sovellettuna rakennusten energiatehokkuus- ja sisäilmastoanalyyseihin on mahdollista jalostaa suuria monimuuttuja-aineistoja tehokkaasti. Laskennallisten menetelmien innovatiivinen käyttö antaa hyvät perusteet tutkia ja kehittää uusia informaatiopalveluja. Tutkijoiden tulee tehdä yhteistyötä loppukäyttäjinä toimivien kiinteistöhallinnan ja -ylläpidon henkilöstön sekä asukkaiden kanssa saavuttaakseen parempia analyysituloksia, helpompaa tulosten tulkintaa ja oikeita johtopäätöksiä tietämyksen hyödyntämiseksi
Radovanovic, Aleksandar. "Concept Based Knowledge Discovery from Biomedical Literature". Thesis, Online access, 2009. http://etd.uwc.ac.za/usrfiles/modules/etd/docs/etd_gen8Srv25Nme4_9861_1272229462.pdf.
Texto completoElsilä, U. (Ulla). "Knowledge discovery method for deriving conditional probabilities from large datasets". Doctoral thesis, University of Oulu, 2007. http://urn.fi/urn:isbn:9789514286698.
Texto completoYu, Zhiguo. "Cooperative Semantic Information Processing for Literature-Based Biomedical Knowledge Discovery". UKnowledge, 2013. http://uknowledge.uky.edu/ece_etds/33.
Texto completoDurbha, Surya Srinivas. "Semantics-enabled framework for knowledge discovery from Earth observation data". Diss., Mississippi State : Mississippi State University, 2006. http://sun.library.msstate.edu/ETD-db/ETD-browse/browse.
Texto completoKulhavý, Lukáš. "Praktické uplatnění technologií data mining ve zdravotních pojišťovnách". Master's thesis, Vysoká škola ekonomická v Praze, 2010. http://www.nusl.cz/ntk/nusl-77726.
Texto completoLi, Xin. "Graph-based learning for information systems". Diss., The University of Arizona, 2009. http://hdl.handle.net/10150/193827.
Texto completoTrávníček, Petr. "Aplikace data miningu v podnikové praxi". Master's thesis, Vysoká škola ekonomická v Praze, 2011. http://www.nusl.cz/ntk/nusl-164048.
Texto completoAsenjo, Juan C. "Data Masking, Encryption, and their Effect on Classification Performance: Trade-offs Between Data Security and Utility". NSUWorks, 2017. http://nsuworks.nova.edu/gscis_etd/1010.
Texto completoIsik, Narin. "Fuzzy Spatial Data Cube Construction And Its Use In Association Rule Mining". Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12606056/index.pdf.
Texto completohence, applications that assist decision-making about spatial data like weather forecasting, traffic supervision, mobile communication, etc. have been introduced. In this thesis, more natural and precise knowledge from spatial data is generated by construction of fuzzy spatial data cube and extraction of fuzzy association rules from it in order to improve decision-making about spatial data. This involves an extensive research about spatial knowledge discovery and how fuzzy logic can be used to develop it. It is stated that incorporating fuzzy logic to spatial data cube construction necessitates a new method for aggregation of fuzzy spatial data. We illustrate how this method also enhances the meaning of fuzzy spatial generalization rules and fuzzy association rules with a case-study about weather pattern searching. This study contributes to spatial knowledge discovery by generating more understandable and interesting knowledge from spatial data by extending spatial generalization with fuzzy memberships, extending the spatial aggregation in spatial data cube construction by utilizing weighted measures, and generating fuzzy association rules from the constructed fuzzy spatial data cube.
He, Yuanchen. "Fuzzy-Granular Based Data Mining for Effective Decision Support in Biomedical Applications". Digital Archive @ GSU, 2006. http://digitalarchive.gsu.edu/cs_diss/12.
Texto completoJunior, Jose Fernando Rodrigues. ""Desenvolvimento de um Framework para Análise Visual de Informações Suportando Data Mining"". Universidade de São Paulo, 2003. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-26092003-122130/.
Texto completoIn the present document are joined the collaborations of many works from the fields of Databases, Knowledge Discovery in Databases, Data Mining, and Computer-based Information Visualization, collaborations that, together, define the structure of the research theme and the work of the Masters Dissertation presented herein. This research topic is the Information Visualization discipline, and its relevant theory is reviewed and related to support the concluding activities, both theoretical and practical, reported in this work. The referred work, anchored by the theoretical substance that was studied, makes several contributions to the science in investigation, the Information Visualization, presenting them through formalized proposals described across this text, and through practical results in the form of software enabled to the visual exploration of information. The presented ideas are based on the visual exhibition of numeric analysis, named basic statistics, frequency analysis (Frequency Plot), and according to a relevance analysis (Relevance Plot). There are also reported the contributions to the FastMapDB tool, a visual exploration tool built by the Grupo de Bases de Dados e Imagens do ICMC-USP, the performed enhancements are listed as achieved results in the text. Also, it is presented the Framework, as previewed in this work's original proposal, projected to allow the construction of visual analysis tools; besides its description are listed its architecture, characteristics and utilization. At last, it is described the visualization Pipeline that emerges from the joining of the visualization Framework and the FastMapDB tool. The work ends with a brief analysis of the Information Visualization science based on the studied literature, it is delineated a scenario of the state of the art of this discipline along with suggestions for future work.
Bastos, Pedro. "Inferência de propriedades químicas do algodão através de técnicas de data mining". Master's thesis, Universidade do Minho, 2003. http://hdl.handle.net/10198/1048.
Texto completoThis work describes how the data mining tool named Clementine, can be used in knowleged extraction of phisical and chemical properties in cotton fibre. The resultes achieved demonstrate how the data mining tools can be used to establish, in a efficient way, all the existent relations between the fibre properties. The technological development enabled the measurement of cotton staple fibres properties like length, micronaire, uniformity, strength, elongation, color and trash contents. This is obtained through the use of HVI instruments, providing rapid and reliable results. However, with regard to the study of the chemical properties, the results are obtained by using more time consuming and much more expensive laboratory methods, although, sometimes they are completely ignored by all the agents envolved in the transformation process. This means that the studies about all existent relationships between the fisical and chemical properties are discarded. This knowledge is very important, because chemical properties affect the process of fiber transformation. In this way, by using Clementine it is possible to obtain relations between diferent types of cotton fibres, supported by the creation of rules using artificial intelligence. In this study, several data mining techniques available in the Clementine data mining system are used. Data mining consists of a step in the knowledge discovery process (KDD), a process that aims to discover associations between data sets. The tool includes advanced modelling techniques, based in artificial intelligence, extracting of the existing data, complex relationships, as well as possible association rules between them. This helps to automatize processes such as prediction, estimation and classification, that can be used to provide expert decision support.