About: Applications of Methods of Knowledge Engineering in Data Mining

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An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Projekt, within Data Space : linked.opendata.cz associated with source document(s)

Attributes	Values
rdf:type	http://linked.opendata.cz/ontology/domain/vavai/Projekt
rdfs:seeAlso	http://www.isvav.cz/projectDetail.do?rowId=GA201/08/0802
Description	Knowledge discovery in databases (KDD) is an important part of informatics with many relations to computer science. KDD is the analysis of (often large) observational data sets to find unsuspected relationships and to summarize the data in novel ways tha (en) Významnou aplikační oblastí informatiky s mnoha vazbami na teoretický výzkum je dobývání znalostí z databází (DZD). Jde o analýzu rozsáhlých observačních dat s cílem nalézt netušené vztahy a sumarizovat data novými způsoby tak, že jsou srozumitelná a uži
Title	Applications of Methods of Knowledge Engineering in Data Mining (en) Aplikace metod znalostního inženýrství při dobývání znalostí z databází
skos:notation	GA201/08/0802
http://linked.open...avai/cep/aktivita	Standard projects
http://linked.open...kovaStatniPodpora	http://linked.opendata.cz/resource/domain/vavai/projekt/GA201%2F08%2F0802/celkovaStatniPodpora
http://linked.open...ep/celkoveNaklady	http://linked.opendata.cz/resource/domain/vavai/projekt/GA201%2F08%2F0802/celkoveNaklady
http://linked.open...datumDodatniDoRIV	2014-01-30 (xsd:date)
http://linked.open...i/cep/druhSouteze	VS - Veřejná soutěž ve výzkumu a vývoji
http://linked.open...ep/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open.../cep/fazeProjektu	92546618
http://linked.open...ai/cep/hlavniObor	IN - Informatika
http://linked.open...hodnoceniProjektu	U - Uspěl podle zadání, tj. byly splněny cíle a jeho předpokládané výsledky uvedené ve smlouvě / rozhodnutí o poskytnutí podpory
http://linked.open...vai/cep/kategorie	ZV - Základní výzkum
http://linked.open.../cep/klicovaSlova	knowledge; engineering; knowledge; discovery; in; databases; mathematical; logic (en)
http://linked.open...ep/partnetrHlavni	Fakulta informatiky a statistiky
http://linked.open...inujicichPrijemcu	0 (xsd:int)
http://linked.open...cep/pocetPrijemcu	1 (xsd:int)
http://linked.open...ocetSpoluPrijemcu	1 (xsd:int)
http://linked.open.../pocetVysledkuRIV	90 (xsd:int)
http://linked.open...enychVysledkuVRIV	90 (xsd:int)
http://linked.open...lneniVMinulemRoce	2012-03-30 (xsd:date)
http://linked.open.../prideleniPodpory	http://linked.opendata.cz/resource/domain/vavai/cep/prideleniPodpory/201%2F08%2F0802
http://linked.open...iciPoslednihoRoku	2012
http://linked.open...atUdajeProjZameru	2013
http://linked.open.../vavai/cep/soutez	SGA02008GA-ST
http://linked.open...usZobrazovaneFaze	DUU
http://linked.open...ai/cep/typPojektu	P - Projekt výzkumu a vývoje financovaný ze státního rozpočtu
http://linked.open...ep/ukonceniReseni	2012-12-31 (xsd:date)
http://linked.open.../cep/vedlejsiObor	BB - Aplikovaná statistika, operační výzkum BA - Obecná matematika
http://linked.open...ep/zahajeniReseni	2008-01-01 (xsd:date)
http://linked.open...jektu+dodavatelem	Přínosem projektu je rozvoj metod a nástrojů pro dobývání znalostí, zejména metod v nástroji LISp-Miner a prezentace nastrojem SEWEBAR. Metody jsou doplněny potřebným formálním rámcem a ověřeny na řadě studií nad konkrétními daty. Mezi výstupy patří vedle publikací také softwarové nástroje, dostupné na webu, ty byly použity i ve výuce. Výstupy jsou adekvátně shrnuty v závěrečné kartě projektu. (cs) The project contributes data mining methods and tools, especially methods in the LISp-Miner tool and presentations in the SEWEBAR tool. The methods were accompanied by related theory and case studies on real data. Besides publications, the outputs include software tools available on the web, also used in teaching activities. The outputs are appropriately described in the project summary card. (en)
http://linked.open...tniCyklusProjektu	ZBBBKU
http://linked.open.../cep/klicoveSlovo	knowledge databases discovery engineering in knowledge mathematical
is http://linked.open...vavai/riv/projekt of	Dynamic Classifier Systems and their Applications to Random Forest Ensembles Boosted Neural Networks in Evolutionary Computation Combinatorial Development of Solid Catalytic Materials. Design of High Throughput Experiments, Data Analysis, Data Mining Atherosclerosis Risk Assessment Using Rule-Based Approach Neural Networks as Surrogate Models for Measurements in Optimization Algorithms Assessing the Usability of Predictions of Different Regression Models Dynamic Classifier Aggregation using Fuzzy Integral with Interaction-Sensitive Fuzzy Measure Evolutionary Optimization of Catalysts Assisted by Neural-Network Learning Mining and Post-Processing of Association Rules in the Atherosclerosis Risk Domain ETree Miner: A New GUHA Procedure for Building Exploration Trees Estimation of distribution algorithms with copula-based models Generator Approach to Evolutionary Optimization of Catalysts and its Integration with Surrogate Modeling Instant Feedback on Discovered Association Rules with PMML-Based Query-by-Example LM TaskPooler for batch processing of data-mining task in background On the complementarity of OLAP and rich associations mining SEWEBAR-CMS as a support for teaching data mining Surrogate Model for Mixed-Variables Evolutionary Optimization Based on GLM and RBF Networks Consideration on a Formal Frame for Data Mining Dealing with Domain Knowledge in Association Rules Mining - Several Experiments Omnipack Case Study - Competitive Intelligence Supported by Semantic Annotation LAREDAM - Considerations on System of Local Analytical Reports from Data Mining Multirelational GUHA Method and Genetic Data Classes of Association Rules: An Overview Definability of Association Rules and Tables of Critical Frequencies Representation and Dimensionality Reduction of Semantically Enriched Clickstreams Combining Image Captions and Visual Analysis for Image Concept Classification Semantic Annotation and Linking of Competitive Intelligence Reports for Business Clusters Remarks to Logical Aspects of Measures of Interestingness of Association Rules Implementace GUHA rozhodovacích stromů Data Mining in Atherosclerosis Risk Factor Data (Chapter XVIII) Ontology-Drive Data Preparation for Association Mining Tvorba exploračních stromů v systému Ferda SEWEBAR Tinnitus - Project Presentation Semantic Analytical Reports: A Framework for Post-processing data Mining Results Action Rules and the GUHA Method: Preliminary Considerations and Results Dealing with Background Knowledge in the SEWEBAR Project Considerations on Logical Calculi for Dealing with Knowledge in Data Mining Vyhledávání v digitálních zdrojích v přirozeném jazyce The Role of ICT in Business Inovation Building and Integrating Competitive Intelligence Reports Using the Topic Map Technology Topic maps for Association Rule Mining The GUHA method and observational calculi: tools for data mining Rozšíření systému LISp-Miner o distribuované řešení úloh prostřednictvím PC-Gridu SEWEBAR - Cardio Project The GUHA method and its meaning for data mining Association rules postprocessing via meta-learning Logical Aspects of the Measures of Interestingness of Association Rules Meta-learning for Post-processing of Association Rules Meta-learning for Post-processing of Association Rules in the Atherosclerosis Risk Domain On the Development of the Knowledge and Information Technology Mining for Assocation and Decision Rules using the LISp-Miner System Distributed Data-Mining in the LISp-Miner System Using Techila Grid SEWEBAR-CMS: semantic analytical report authoring for data mining results Elicitation of Background Knowledge for Data Mining XML Schema and Topic Map Ontology for Formalization of Background Knowledge in Data Mining (Full Paper)

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