메뉴 건너뛰기




Volumn , Issue , 2011, Pages 87-94

MINETRAC: Mining flows for unsupervised analysis & semi-supervised classification

Author keywords

Evidence Accumulation; Hierarchical Clustering; Semi Supervised Traffic Classification; Sub Space Clustering; Unsupervised Traffic Analysis

Indexed keywords

EVIDENCE ACCUMULATION; HIER-ARCHICAL CLUSTERING; SEMI-SUPERVISED; SUB-SPACES; UNSUPERVISED TRAFFIC ANALYSIS;

EID: 80054970530     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (32)

References (19)
  • 1
    • 77950369345 scopus 로고    scopus 로고
    • Data clustering: 50 years beyond K-means
    • A. K. Jain, "Data Clustering: 50 Years Beyond K-Means", in Pattern Recognition Letters, vol. 31 (8), pp. 651-666, 2010.
    • (2010) Pattern Recognition Letters , vol.31 , Issue.8 , pp. 651-666
    • Jain, A.K.1
  • 2
    • 17044376078 scopus 로고    scopus 로고
    • Subspace clustering for high dimensional data: A review
    • L. Parsons et al., "Subspace Clustering for High Dimensional Data: a Review", in ACM SIGKDD Expl. Newsletter, vol. 6 (1), pp. 90-105, 2004.
    • (2004) ACM SIGKDD Expl. Newsletter , vol.6 , Issue.1 , pp. 90-105
    • Parsons, L.1
  • 3
    • 21244468777 scopus 로고    scopus 로고
    • Combining multiple clusterings using evidence accumulation
    • A. Fred et al., "Combining Multiple Clusterings using Evidence Accumulation", in IEEE Trans. Patt. Anal. & Mach. Intell., vol. 27 (6), 2005.
    • (2005) IEEE Trans. Patt. Anal. & Mach. Intell. , vol.27 , Issue.6
    • Fred, A.1
  • 4
    • 84869166587 scopus 로고    scopus 로고
    • Internet traffic classification using Bayesian analysis techniques
    • A. Moore, D. Zuev, "Internet Traffic Classification using Bayesian Analysis Techniques", in Proc. ACM SIGMETICS, 2005.
    • (2005) Proc. ACM SIGMETICS
    • Moore, A.1    Zuev, D.2
  • 5
    • 14944383480 scopus 로고    scopus 로고
    • Class-of-service mapping for QoS: A statistical signature-based approach to IP traffic classification
    • M. Roughan et al., "Class-of-Service Mapping for QoS: a Statistical Signature-Based Approach to IP Traffic Classification", in IMW, 2004.
    • (2004) IMW
    • Roughan, M.1
  • 6
    • 33750283653 scopus 로고    scopus 로고
    • A preliminary performance comparison of five machine learning algorithms for practical IP traffic flow classification
    • N. Williams et al., "A Preliminary Performance Comparison of Five Machine Learning Algorithms for Practical IP Traffic Flow Classification", in ACM SIGCOMM Computer Communication Review, vol. 36 (5), 2006.
    • (2006) ACM SIGCOMM Computer Communication Review , vol.36 , Issue.5
    • Williams, N.1
  • 7
    • 84892630808 scopus 로고    scopus 로고
    • Accurate, fine-grained classification of P2P-TV applications by simply counting packets
    • S. Valenti et al., "Accurate, Fine-Grained Classification of P2P-TV Applications by Simply Counting Packets", in Proc. 1st TMA Workshop, 2009.
    • (2009) Proc. 1st TMA Workshop
    • Valenti, S.1
  • 8
    • 70349653299 scopus 로고    scopus 로고
    • Traffic classification using clustering algorithms
    • J. Erman et al., "Traffic Classification using Clustering Algorithms", in Proc. MineNet, 2006.
    • (2006) Proc. MineNet
    • Erman, J.1
  • 9
    • 67650308093 scopus 로고    scopus 로고
    • Semi-supervised network traffic classification
    • J. Erman et al., "Semi-Supervised Network Traffic Classification", in Proc. ACM SIGMETRICS, 2007.
    • (2007) Proc. ACM SIGMETRICS
    • Erman, J.1
  • 10
    • 62849120844 scopus 로고    scopus 로고
    • A survey of techniques for internet traffic classification using machine learning
    • T. Nguyen et al., "A Survey of Techniques for Internet Traffic Classification using Machine Learning", in IEEE Comm, Surv. & Tut., 2008.
    • (2008) IEEE Comm, Surv. & Tut.
    • Nguyen, T.1
  • 11
    • 0041965980 scopus 로고    scopus 로고
    • Cluster ensembles - A knowledge reuse framework for combining multiple partitions
    • A. Strehl et al., "Cluster Ensembles - a Knowledge Reuse Framework for Combining Multiple Partitions", in J. Mach. Lear. Res., vol. 3, 2002.
    • (2002) J. Mach. Lear. Res. , vol.3
    • Strehl, A.1
  • 12
    • 0000550189 scopus 로고    scopus 로고
    • A density-based algorithm for discovering clusters in large spatial databases with noise
    • M. Ester et al., "A Density-based Algorithm for Discovering Clusters in Large Spatial Databases with Noise", in Proc. ACM SIGKDD, 1996.
    • (1996) Proc. ACM SIGKDD
    • Ester, M.1
  • 13
    • 85012236181 scopus 로고    scopus 로고
    • A framework for clustering evolving data streams
    • C. Aggarwal, J. Han, J. Wang, P. Yu, "A Framework for Clustering Evolving Data Streams", in Proc. VLDB, 2003.
    • (2003) Proc. VLDB
    • Aggarwal, C.1    Han, J.2    Wang, J.3    Yu, P.4
  • 15
    • 84872294071 scopus 로고    scopus 로고
    • GT: Picking up the truth from the ground for internet traffic
    • F. Gringoli et al., "GT: Picking Up the Truth from the Ground for Internet Traffic", in ACM Comp. Comm. Review, vol. 39 (5), pp. 13-18, 2009.
    • (2009) ACM Comp. Comm. Review , vol.39 , Issue.5 , pp. 13-18
    • Gringoli, F.1
  • 18
    • 80054985820 scopus 로고    scopus 로고
    • On the validation of traffic classification algorithms
    • G. Szabo et al., "On the Validation of Traffic Classification Algorithms", in Proc. 9th PAM, 2008.
    • (2008) Proc. 9th PAM
    • Szabo, G.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.