메뉴 건너뛰기




Volumn 9, Issue 6, 2010, Pages 935-957

Unsupervised learning based distributed detection of global anomalies

Author keywords

combining models; Distributed anomaly detection; global anomalies

Indexed keywords


EID: 78149346488     PISSN: 02196220     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0219622010004172     Document Type: Article
Times cited : (6)

References (40)
  • 1
    • 0003239461 scopus 로고    scopus 로고
    • On the accuracy of meta-learning for scalable data mining
    • P. Chan and S. Stolfo, On the accuracy of meta-learning for scalable data mining, J. Intell. Integr. Inform., 1998.
    • (1998) J. Intell. Integr. Inform.
    • Chan, P.1    Stolfo, S.2
  • 2
    • 0001920992 scopus 로고    scopus 로고
    • Human expert-level performance on a scientific image analysis task by a system using combined artificial neural networks
    • K. J. Cherkauer, Human expert-level performance on a scientific image analysis task by a system using combined artificial neural networks, in Working Notes of the AAAI Workshop on Integrating Multiple Learned Models (1996), pp. 15-21.
    • (1996) Working Notes of the AAAI Workshop on Integrating Multiple Learned Models , pp. 15-21
    • Cherkauer, K.J.1
  • 3
    • 10444271270 scopus 로고    scopus 로고
    • Ensembles of classifiers for handwritten word recognition
    • S. Gunter and H. Bunke, Ensembles of classifiers for handwritten word recognition, Int. J. Doc. Anal. Recognit. 5 (2001) 1433-2833.
    • (2001) Int. J. Doc. Anal. Recognit , vol.5 , pp. 1433-2833
    • Gunter, S.1    Bunke, H.2
  • 4
    • 0037332504 scopus 로고    scopus 로고
    • Medical diagnosis with C4. 5 rule preceded by artificial neural network ensemble
    • Z.-H. Zhou and Y. Jiang, Medical diagnosis with C4. 5 rule preceded by artificial neural network ensemble, IEEE Trans. Inf. Technol. Biom ed. 7 (2003) 37-42.
    • (2003) IEEE Trans. Inf. Technol. Biom Ed. , vol.7 , pp. 37-42
    • Zhou, Z.-H.1    Jiang, Y.2
  • 5
    • 58149235001 scopus 로고    scopus 로고
    • A descriptive framework for the field of data mining and knowledge discovery
    • Y. Peng, G. Kou, Y. Shi and Z. X. Chen, A descriptive framework for the field of data mining and knowledge discovery, Int. J. Inf. Technol. Decis. Mak. (IJITDM) 7 (2008) 639-682.
    • (2008) Int. J. Inf. Technol. Decis. Mak. (IJITDM) , vol.7 , pp. 639-682
    • Peng, Y.1    Kou, G.2    Shi, Y.3    Chen, Z.X.4
  • 6
    • 0001141579 scopus 로고    scopus 로고
    • The application of AdaBoost for distributed, scalable and on-line learning
    • W. Fan, S. Stolfo and J. Zhang, The application of AdaBoost for distributed, scalable and on-line learning, in Proc. SIGKDD (1999), pp. 362-366.
    • (1999) Proc. SIGKDD , pp. 362-366
    • Fan, W.1    Stolfo, S.2    Zhang, J.3
  • 7
    • 32344440279 scopus 로고    scopus 로고
    • Feature bagging for outlier detection
    • A. Lazarevic and V. Kumar, Feature bagging for outlier detection, in Proc. SIGKDD (2005).
    • (2005) Proc. SIGKDD
    • Lazarevic, A.1    Kumar, V.2
  • 8
    • 51149094760 scopus 로고    scopus 로고
    • Distributed anomaly detection, using cooperative learners and association rule analysis
    • G. Deshmeh and M. Rahmati, Distributed anomaly detection, using cooperative learners and association rule analysis, Intell. Data Anal. 12 (2008) 339-357.
    • (2008) Intell. Data Anal. , vol.12 , pp. 339-357
    • Deshmeh, G.1    Rahmati, M.2
  • 9
    • 34547324175 scopus 로고    scopus 로고
    • Fuzzy art-based image clustering method for contentbased image retrieval
    • S. Park, K. Seo and D. Jang, Fuzzy art-based image clustering method for contentbased image retrieval, Int. J. Inf. Technol. Decis. Mak. (IJITDM) 6 (2007) 213-233.
    • (2007) Int. J. Inf. Technol. Decis. Mak. (IJITDM) , vol.6 , pp. 213-233
    • Park, S.1    Seo, K.2    Jang, D.3
  • 10
    • 34547338342 scopus 로고    scopus 로고
    • Hierarchical anomaly detection in distributed large-scale sensor networks
    • V. Chatzigiannakis, S. Papavassiliou, M. Grammatikou and B. Maglaris, Hierarchical anomaly detection in distributed large-scale sensor networks, ISCC (2006), pp. 761-767.
    • (2006) ISCC , pp. 761-767
    • Chatzigiannakis, V.1    Papavassiliou, S.2    Grammatikou, M.3    Maglaris, B.4
  • 15
    • 35348828484 scopus 로고    scopus 로고
    • Ensemble methods for anomaly detection and distributed intrusion detection in Mobile Ad-Hoc Networks
    • J. B. D. Cabrera, C. Gutirreza and R. K. Mehraa, Ensemble methods for anomaly detection and distributed intrusion detection in Mobile Ad-Hoc Networks, Inf. Fusion 9 (2008) 96-119.
    • (2008) Inf. Fusion , vol.9 , pp. 96-119
    • Cabrera, J.B.D.1    Gutirreza, C.2    Mehraa, R.K.3
  • 16
    • 0001138328 scopus 로고
    • A k-means clustering algorithm
    • J. A. Hartigan and M. A. Wong, A k-means clustering algorithm, Appl. Stat. 28 (1979) 100-108.
    • (1979) Appl. Stat , vol.28 , pp. 100-108
    • Hartigan, J.A.1    Wong, M.A.2
  • 19
    • 0037172724 scopus 로고    scopus 로고
    • A prediction-based resampling method for estimating the number of clusters in a dataset
    • S. Dudoit and J. Fridlyand, A prediction-based resampling method for estimating the number of clusters in a dataset, Genome Biol. 3 (2002) 1-21.
    • (2002) Genome Biol. , vol.3 , pp. 1-21
    • Dudoit, S.1    Fridlyand, J.2
  • 20
    • 0037399775 scopus 로고    scopus 로고
    • Cluster validation techniques for genome expression data
    • N. Bolshakova and F. Azuaje, Cluster validation techniques for genome expression data, Signal Process. 8 (2003) 825-833.
    • (2003) Signal Process , vol.8 , pp. 825-833
    • Bolshakova, N.1    Azuaje, F.2
  • 21
    • 0035283313 scopus 로고    scopus 로고
    • Robust classification for imprecise environments
    • F. Provost and T. Fawcett, Robust classification for imprecise environments, Mach. Learn. 42 (2001) 203-231.
    • (2001) Mach. Learn , vol.42 , pp. 203-231
    • Provost, F.1    Fawcett, T.2
  • 22
    • 0031211090 scopus 로고    scopus 로고
    • A decision theoretic generalization of on-line learning and an application to boosting
    • Y. Freund and E. R. Schapire, A decision theoretic generalization of on-line learning and an application to boosting, J. Comput. Syst. Sci. 55 (1997) 119-139.
    • (1997) J. Comput. Syst. Sci. , vol.55 , pp. 119-139
    • Freund, Y.1    Schapire, E.R.2
  • 23
    • 0030211964 scopus 로고    scopus 로고
    • Bagging predictors
    • L. Breiman, Bagging predictors, Mach. Learn. 24 (1996) 123-140.
    • (1996) Mach. Learn , vol.24 , pp. 123-140
    • Breiman, L.1
  • 24
    • 0346786584 scopus 로고    scopus 로고
    • Arcing classifiers
    • L. Breiman, Arcing classifiers, Ann. Statist. 26 (1998) 801-849.
    • (1998) Ann. Statist , vol.26 , pp. 801-849
    • Breiman, L.1
  • 25
    • 0035478854 scopus 로고    scopus 로고
    • Random forests
    • L. Breiman, Random forests, Mach. Learn. 45 (2001) 5-32.
    • (2001) Mach. Learn , vol.45 , pp. 5-32
    • Breiman, L.1
  • 26
    • 34548463136 scopus 로고    scopus 로고
    • Principal component analysis
    • 2nd edn.
    • I. T. Jolliffe, Principal component analysis, in Springer Series in Statistics, 2nd edn., XXIX, Vol. 48 (2002) 20-28.
    • (2002) Springer Series in Statistics , vol.29-48 , pp. 20-28
    • Jolliffe, I.T.1
  • 27
    • 84992322729 scopus 로고
    • Error-correcting output coding corrects bias and variance
    • E. Kong and T. Dietterich, Error-correcting output coding corrects bias and variance, in Proc. ICML (1995), pp. 313-321.
    • (1995) Proc. ICML , pp. 313-321
    • Kong, E.1    Dietterich, T.2
  • 28
    • 0037753593 scopus 로고    scopus 로고
    • Ph. D. thesis, Delft University of Technology, June
    • D. M. J. Tax, One-class classification, Ph. D. thesis, Delft University of Technology, June, (2001).
    • (2001) One-class Classification
    • Tax, D.M.J.1
  • 29
    • 0029276335 scopus 로고
    • A randomized linear-time algorithm to find minimum spanning trees
    • D. Karger, P. Klein and R. Tarjan, A randomized linear-time algorithm to find minimum spanning trees, J. ACM 42 (1995) 321-328.
    • (1995) J. ACM , vol.42 , pp. 321-328
    • Karger, D.1    Klein, P.2    Tarjan, R.3
  • 30
    • 58149234998 scopus 로고    scopus 로고
    • Web mining: A survey of current research, techniques, and software
    • Q. Zhang and R. S. Segal, Web mining: A survey of current research, techniques, and software, Int. J. Inf. Technol. Decis. Mak. (IJITDM) 7 (2008) 683-720.
    • (2008) Int. J. Inf. Technol. Decis. Mak. (IJITDM) , vol.7 , pp. 683-720
    • Zhang, Q.1    Segal, R.S.2
  • 33
    • 27144550416 scopus 로고    scopus 로고
    • SMOTEBoost: Improving the prediction of minority class in boosting
    • N. V. Chawla, A. Lazarevic, Lawrence O. Hall and Kevin Bowyer, SMOTEBoost: Improving the prediction of minority class in boosting, in Proc. PKDD (2003).
    • (2003) Proc. PKDD
    • Chawla, N.V.1    Lazarevic, A.2    Hall, L.O.3    Bowyer, K.4
  • 34
    • 34547352583 scopus 로고    scopus 로고
    • Data mining via minimal spanning tree clustering for prolonging lifetime of wireless sensor networks
    • X. Li, J. He and X. Li, Data mining via minimal spanning tree clustering for prolonging lifetime of wireless sensor networks, Int. J. Inform. Technol. Decis. Mak. (IJITDM) 2 (2007) 235-251.
    • (2007) Int. J. Inform. Technol. Decis. Mak. (IJITDM) , vol.2 , pp. 235-251
    • Li, X.1    He, J.2    Li, X.3
  • 35
    • 0141990688 scopus 로고    scopus 로고
    • Improved rooftop detection in aerial images with machine learning
    • N. M. Maloof, P. Langley, T. Binford, R. Nevatia and S. Sage, Improved rooftop detection in aerial images with machine learning, Mach. Learn. 53 (2003) 157-191.
    • (2003) Mach. Learn , vol.53 , pp. 157-191
    • Maloof, N.M.1    Langley, P.2    Binford, T.3    Nevatia, R.4    Sage, S.5
  • 36
    • 0041965980 scopus 로고    scopus 로고
    • Cluster ensembles-A knowledge reuse framework for combining multiple partitions
    • A. Strehl, J. Ghosh and C. Cardie, Cluster ensembles-A knowledge reuse framework for combining multiple partitions, JMLR 3 (2003) 583-617.
    • (2003) JMLR , vol.3 , pp. 583-617
    • Strehl, A.1    Ghosh, J.2    Cardie, C.3
  • 37
    • 85132247975 scopus 로고    scopus 로고
    • Findout: Finding outliers in very large datasets
    • D. Yu, G. Sheikholeslami and A. Zhang, Findout: Finding outliers in very large datasets, J. Knowl. Inf. Syst. 4 (2002) 387-412.
    • (2002) J. Knowl. Inf. Syst. , vol.4 , pp. 387-412
    • Yu, D.1    Sheikholeslami, G.2    Zhang, A.3
  • 38
    • 0035336998 scopus 로고    scopus 로고
    • Tw-phase clustering process for outliers detection
    • M. F. Jiang, S. S. Tseng and C. M. Su, Tw-phase clustering process for outliers detection, Pattern Recogn. Lett. 22 (2001) 691-700.
    • (2001) Pattern Recogn. Lett. , vol.22 , pp. 691-700
    • Jiang, M.F.1    Tseng, S.S.2    Su, C.M.3
  • 40
    • 34547354165 scopus 로고    scopus 로고
    • Parallel processing of OLAP queries using a cluster of workstations
    • S. Dehuri and R. Mall, Parallel processing of OLAP queries using a cluster of workstations, Int. J. Inf. Technol. Decis. Mak. (IJITDM) 6 (2007) 279-299.
    • (2007) Int. J. Inf. Technol. Decis. Mak. (IJITDM) , vol.6 , pp. 279-299
    • Dehuri, S.1    Mall, R.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.