dc.contributor.advisor | Kofod-Petersen, Anders | nb_NO |
dc.contributor.author | He, Yu | nb_NO |
dc.date.accessioned | 2014-12-19T13:40:07Z | |
dc.date.available | 2014-12-19T13:40:07Z | |
dc.date.created | 2013-10-12 | nb_NO |
dc.date.issued | 2013 | nb_NO |
dc.identifier | 655617 | nb_NO |
dc.identifier | ntnudaim:9433 | nb_NO |
dc.identifier.uri | http://hdl.handle.net/11250/253323 | |
dc.description.abstract | This paper looks at the current state-of-the-art scalable real-time data miningsystems, and explores possible improvements to the automated knowledge discov-ery process through potential improvements in feature selection, use of clusteringalgorithms, and the information evaluation process, while still maintaining highscalability and real-time performance. A framework is designed and built to testthe system on real-world data. | nb_NO |
dc.language | eng | nb_NO |
dc.publisher | Institutt for datateknikk og informasjonsvitenskap | nb_NO |
dc.title | Knowledge Discovery in Scalable Real-time Data Mining Systems | nb_NO |
dc.type | Master thesis | nb_NO |
dc.source.pagenumber | 65 | nb_NO |
dc.contributor.department | Norges teknisk-naturvitenskapelige universitet, Fakultet for informasjonsteknologi, matematikk og elektroteknikk, Institutt for datateknikk og informasjonsvitenskap | nb_NO |