Search results for "Knowledge discovery"

showing 5 items of 25 documents

Framework for pedagogical learning analytics

2018

Learning analytics is an emergent technological practice and a multidisciplinary scientific discipline, which goal is to facilitate effective learning and knowledge of learning. In this design science research, I combine knowledge discovery process, a concept of pedagogical knowledge, ethics of learning analytics and microservice architecture. The result is a framework for pedagogical learning analytics. The framework is applied and evaluated in the context of agency analytics. The framework contributes to the practical use of learning analytics.

microserviceknowledge discoverypedagogical learning analyticsstudent agencypedagogical knowledgeGDPRethics
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Detector-based visual analysis of time-series data

2015

neural networkaikasarjatvisualisointimittausgraphical user interfaceknowledge discoverychange-point detectiondata miningneuroverkotvisual analyticsuser interactioncontextaikasarja-analyysimittaustekniikkavisual data explorationkäyttöliittymätihminen-konejärjestelmätenergiantuotantolaitoksetklusterianalyysitiedonlouhintaenergiantuotantobiovoimalatvisualizationclustering
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Intrusion detection applications using knowledge discovery and data mining

2014

pääsynvalvontaintrusion detectionknowledge discoverydata miningvalvontajärjestelmätanomaly detectionbig dataalgoritmitklusterianalyysitietoturvatiedonlouhintakyberturvallisuusverkkohyökkäyksetdimensionality reductionclustering
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Improvements and applications of the elements of prototype-based clustering

2018

Clustering or cluster analysis is an essential part of data mining, machine learning, and pattern recognition. The most popularly applied clustering methods are partitioning-based or prototype-based methods. Prototype-based clustering methods usually have easy implementability and good scalability. These methods, such as K-means clustering, have been used for different applications in various fields. On the other hand, prototype-based clustering methods are typically sensitive to initialization, and the selection of the number of clusters for knowledge discovery purposes is not straightforward. In the era of big data, in high-velocity, ever-growing datasets, which can also be erroneous, outl…

random projectionparallel computingknowledge discoveryclustering initializationminimal learning machinedata miningprototype-based clusteringmachine learningkoneoppiminenbig datarinnakkaiskäsittelyklusterianalyysitiedonlouhintarobust clusteringK-means
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Research literature clustering using diffusion maps

2013

We apply the knowledge discovery process to the mapping of current topics in a particular field of science. We are interested in how articles form clusters and what are the contents of the found clusters. A framework involving web scraping, keyword extraction, dimensionality reduction and clustering using the diffusion map algorithm is presented. We use publicly available information about articles in high-impact journals. The method should be of use to practitioners or scientists who want to overview recent research in a field of science. As a case study, we map the topics in data mining literature in the year 2011. peerReviewed

ta113kirjallisuuskatsausklusterointiComputer scienceProcess (engineering)Dimensionality reductiondiffuusiokuvausta111Diffusion mapKeyword extractionliterature mappingdiffusion mapKnowledge discovery processLibrary and Information Sciencescomputer.software_genreData scienceField (geography)Computer Science ApplicationsKnowledge extractionTiedonhavaitsemisprosessitiedonlouhintaCluster analysiscomputerWeb scrapingclustering
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