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dc.contributor.author이병호-
dc.date.accessioned2018-03-20T00:55:37Z-
dc.date.available2018-03-20T00:55:37Z-
dc.date.issued2012-09-
dc.identifier.citationKSII Transactions on Internet and Information Systems, Sep 2012, 6(9), P.2191-2201, 11P.en_US
dc.identifier.issn1976-7277-
dc.identifier.urihttp://www.itiis.org/digital-library/manuscript/396-
dc.identifier.urihttp://hdl.handle.net/20.500.11754/49198-
dc.description.abstractTraffic classification seeks to assign packet flows to an appropriate quality of service (QoS) class based on flow statistics without the need to examine packet payloads. Classification proceeds in two steps. Classification rules are first built by analyzing traffic traces, and then the classification rules are evaluated using test data. In this paper, we use self-organizing map and K-means clustering as unsupervised machine learning methods to identify the inherent classes in traffic traces. Three clusters were discovered, corresponding to transactional, bulk data transfer, and interactive applications. The K-nearest neighbor classifier was found to be highly accurate for the traffic data and significantly better compared to a minimum mean distance classifier.en_US
dc.language.isoenen_US
dc.publisher한국인터넷정보학회en_US
dc.subjectTraffic classificationen_US
dc.subjectunsupervised learningen_US
dc.subjectk-nearest neighboren_US
dc.subjectclusteringen_US
dc.titleA Low Complexity PTS Technique using Threshold for PAPR Reduction in OFDM Systemsen_US
dc.typeArticleen_US
dc.relation.no9-
dc.relation.volume6-
dc.identifier.doi10.3837/tiis.2012.09.012-
dc.relation.page2191-2201-
dc.relation.journalKSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS-
dc.contributor.googleauthorLim, Dai Hwan-
dc.contributor.googleauthorRhee, Byung Ho-
dc.contributor.googleauthor임대환-
dc.contributor.googleauthor이병호-
dc.relation.code2012221065-
dc.sector.campusS-
dc.sector.daehakCOLLEGE OF ENGINEERING[S]-
dc.sector.departmentDEPARTMENT OF COMPUTER SCIENCE-
dc.identifier.pidbhrhee-
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > COMPUTER SCIENCE(컴퓨터소프트웨어학부) > Articles
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