메뉴 건너뛰기




Volumn 100, Issue 2-3, 2015, Pages 509-531

A decomposition of the outlier detection problem into a set of supervised learning problems

Author keywords

Machine learning; Outlier detection; Outlier explanations

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLUSTERING ALGORITHMS; DATA HANDLING; DATA MINING; LEARNING SYSTEMS;

EID: 84939270771     PISSN: 08856125     EISSN: 15730565     Source Type: Journal    
DOI: 10.1007/s10994-015-5507-y     Document Type: Article
Times cited : (55)

References (38)
  • 1
    • 33749539634 scopus 로고    scopus 로고
    • Abe, N., Zadrozny, B., & Langford, J. (2006). Outlier detection by active learning. In Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 504–509). ACM
    • Abe, N., Zadrozny, B., & Langford, J. (2006). Outlier detection by active learning. In Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 504–509). ACM.
  • 2
    • 49049108359 scopus 로고    scopus 로고
    • Achtert, E., Kriegel, H. P., & Zimek, A. (2008). ELKI: A software system for evaluation of subspace clustering algorithms. In Scientific and statistical database management. Lecture notes in computer science (Vol. 5069, pp. 580–585). Berlin, Heidelberg: Springer
    • Achtert, E., Kriegel, H. P., & Zimek, A. (2008). ELKI: A software system for evaluation of subspace clustering algorithms. In Scientific and statistical database management. Lecture notes in computer science (Vol. 5069, pp. 580–585). Berlin, Heidelberg: Springer. doi:10.1007/978-3-540-69497-7_41.
  • 5
    • 0038969998 scopus 로고    scopus 로고
    • Outlier detection for high dimensional data
    • Aggarwal, C. C., & Yu, P. S. (2001). Outlier detection for high dimensional data. SIGMOD Record, 30(2), 37–46. doi:10.1145/376284.375668.
    • (2001) SIGMOD Record , vol.30 , Issue.2 , pp. 37-46
    • Aggarwal, C.C.1    Yu, P.S.2
  • 6
    • 77951175762 scopus 로고    scopus 로고
    • Rule ensembles for multi-target regression
    • Aho, T., Zenko, B., & Dzeroski, S. (2009). Rule ensembles for multi-target regression. In ICDM (pp. 21–30).
    • (2009) In ICDM , pp. 21-30
    • Aho, T.1    Zenko, B.2    Dzeroski, S.3
  • 12
    • 0039253819 scopus 로고    scopus 로고
    • Lof: Identifying density-based local outliers
    • Breunig, M. M., Kriegel, H. P., Ng, R. T., & Sander, J. (2000). Lof: Identifying density-based local outliers. ACM Sigmod Record, 29(2), 93–104.
    • (2000) ACM Sigmod Record , vol.29 , Issue.2 , pp. 93-104
    • Breunig, M.M.1    Kriegel, H.P.2    Ng, R.T.3    Sander, J.4
  • 14
    • 29644438050 scopus 로고    scopus 로고
    • Statistical comparisons of classifiers over multiple data sets
    • Demšar, J. (2006). Statistical comparisons of classifiers over multiple data sets. The Journal of Machine Learning Research, 7, 1–30.
    • (2006) The Journal of Machine Learning Research , vol.7 , pp. 1-30
    • Demšar, J.1
  • 15
    • 58149109880 scopus 로고    scopus 로고
    • Efficient clustering-based outlier detection algorithm for dynamic data stream. In Fifth international conference on fuzzy systems and knowledge discovery
    • Elahi, M., Li, K., Nisar, W., Lv, X., & Wang, H. (2008). Efficient clustering-based outlier detection algorithm for dynamic data stream. In Fifth international conference on fuzzy systems and knowledge discovery, FSKD ’08 (Vol. 5, pp. 298–304). doi:10.1109/FSKD.2008.374.
    • (2008) FSKD ’08 , vol.5 , pp. 298-304
    • Elahi, M.1    Li, K.2    Nisar, W.3    Lv, X.4    Wang, H.5
  • 17
    • 84939257716 scopus 로고    scopus 로고
    • Anomaly detection
    • CRC Pres
    • Goldstein, M. (2014). Anomaly detection. In M. Hofmann & R. Klinkenberg (Eds.), RapidMiner—Data mining use cases and business analytics applications (pp. 409–436). CRC Press.
    • (2014) M. Hofmann & R. Klinkenberg , pp. 409-436
    • Goldstein, M.1    Goldstein, M.2
  • 18
    • 84864859588 scopus 로고    scopus 로고
    • Outlier detection using replicator neural networks. In Y. Kambayashi, W. Winiwarter, & M. Arikawa (Eds.)
    • 170–180. Berlin, Heidelberg: Springer
    • Hawkins, S., He, H., Williams, G., & Baxter, R. (2002). Outlier detection using replicator neural networks. In Y. Kambayashi, W. Winiwarter, & M. Arikawa (Eds.), Data warehousing and knowledge discovery. Lecture notes in computer science (pp. 170–180). Berlin, Heidelberg: Springer. doi:10.1007/3-540-46145-0_17.
    • (2002) Data warehousing and knowledge discovery. Lecture notes in computer science
    • Hawkins, S.1    He, H.2    Williams, G.3    Baxter, R.4
  • 19
    • 0037410488 scopus 로고    scopus 로고
    • Discovering cluster-based local outliers
    • He, Z., Xu, X., & Deng, S. (2003). Discovering cluster-based local outliers. Pattern Recognition Letters, 24(9), 1641–1650.
    • (2003) Pattern Recognition Letters , vol.24 , Issue.9 , pp. 1641-1650
    • He, Z.1    Xu, X.2    Deng, S.3
  • 21
    • 7544223741 scopus 로고    scopus 로고
    • A survey of outlier detection methodologies
    • Hodge, V. J., & Austin, J. (2004). A survey of outlier detection methodologies. Artificial Intelligence Review, 22(2), 85–126.
    • (2004) Artificial Intelligence Review , vol.22 , Issue.2 , pp. 85-126
    • Hodge, V.J.1    Austin, J.2
  • 22
    • 84939283321 scopus 로고    scopus 로고
    • Knorr, E. M., & Ng, R. T. (1999). Finding intensional knowledge of distance-based outliers. In Proceedings of the 25th international conference on very large data bases, VLDB ’99 (Vol. 99, pp. 211–222). San Francisco, CA: Morgan Kaufmann Publishers Inc
    • Knorr, E. M., & Ng, R. T. (1999). Finding intensional knowledge of distance-based outliers. In Proceedings of the 25th international conference on very large data bases, VLDB ’99 (Vol. 99, pp. 211–222). San Francisco, CA: Morgan Kaufmann Publishers Inc.
  • 23
    • 74549182696 scopus 로고    scopus 로고
    • Kriegel, H. P., Kröger, P., Schubert, E., & Zimek, A. (2009). Loop: Local outlier probabilities. In Proceedings of the 18th ACM conference on information and knowledge management (pp. 1649–1652). ACM
    • Kriegel, H. P., Kröger, P., Schubert, E., & Zimek, A. (2009). Loop: Local outlier probabilities. In Proceedings of the 18th ACM conference on information and knowledge management (pp. 1649–1652). ACM.
  • 24
    • 84874057277 scopus 로고    scopus 로고
    • Kriegel, H. P., Kröger, P., Schubert, E., & Zimek, A. (2012). Outlier detection in arbitrarily oriented subspaces. In 2012 IEEE 12th international conference on data mining, ICDM ’12 (pp. 379–388). IEEE
    • Kriegel, H. P., Kröger, P., Schubert, E., & Zimek, A. (2012). Outlier detection in arbitrarily oriented subspaces. In 2012 IEEE 12th international conference on data mining, ICDM ’12 (pp. 379–388). IEEE.
  • 26
    • 70350238780 scopus 로고    scopus 로고
    • Mejía-Lavalle, M., & Sánchez Vivar, A. (2009). Outlier detection with explanation facility. In Machine learning and data mining in pattern recognition. Lecture notes in computer science (Vol. 5632, pp. 454–464). Berlin, Heidelberg: Springer
    • Mejía-Lavalle, M., & Sánchez Vivar, A. (2009). Outlier detection with explanation facility. In Machine learning and data mining in pattern recognition. Lecture notes in computer science (Vol. 5632, pp. 454–464). Berlin, Heidelberg: Springer. doi:10.1007/978-3-642-03070-3_34.
  • 28
    • 84910664178 scopus 로고
    • Distribution-free multiple comparisons
    • Nemenyi, P. (1962). Distribution-free multiple comparisons. Biometrics, 18(2), 263.
    • (1962) Biometrics , vol.18 , Issue.2 , pp. 263
    • Nemenyi, P.1
  • 29
    • 0034593047 scopus 로고    scopus 로고
    • Padmanabhan, B., & Tuzhilin, A. (2000). Small is beautiful: Discovering the minimal set of unexpected patterns. In Proceedings of the sixth ACM SIGKDD international conference on knowledge discovery and data mining (pp. 54–63). ACM
    • Padmanabhan, B., & Tuzhilin, A. (2000). Small is beautiful: Discovering the minimal set of unexpected patterns. In Proceedings of the sixth ACM SIGKDD international conference on knowledge discovery and data mining (pp. 54–63). ACM.
  • 30
    • 84939283327 scopus 로고    scopus 로고
    • Pelleg, D., Moore, A. W. (2000). X-means: Extending K-means with efficient estimation of the number of clusters. In Proceedings of the seventeenth international conference on machine learning, ICML ’00 (pp. 727–734). San Francisco, CA: Morgan Kaufmann Publishers Inc
    • Pelleg, D., Moore, A. W. (2000). X-means: Extending K-means with efficient estimation of the number of clusters. In Proceedings of the seventeenth international conference on machine learning, ICML ’00 (pp. 727–734). San Francisco, CA: Morgan Kaufmann Publishers Inc.
  • 31
    • 34548752457 scopus 로고    scopus 로고
    • Pokrajac, D., Lazarevic, A., & Latecki, L. J. (2007). Incremental local outlier detection for data streams. In IEEE symposium on computational intelligence and data mining, CIDM ’07 (pp. 504–515). IEEE
    • Pokrajac, D., Lazarevic, A., & Latecki, L. J. (2007). Incremental local outlier detection for data streams. In IEEE symposium on computational intelligence and data mining, CIDM ’07 (pp. 504–515). IEEE.
  • 33
    • 0006278076 scopus 로고
    • On the use of a friedman-type statistic in balanced and unbalanced block designs
    • Skillings, J. H., & Mack, G. A. (1981). On the use of a friedman-type statistic in balanced and unbalanced block designs. Technometrics, 23(2), 171–177.
    • (1981) Technometrics , vol.23 , Issue.2 , pp. 171-177
    • Skillings, J.H.1    Mack, G.A.2
  • 34
    • 84939283329 scopus 로고    scopus 로고
    • Teng, C. M. (1999). Correcting noisy data. In Proceedings of the sixteenth international conference on machine learning, ICML ’99 (pp. 239–248). San Francisco, CA: Morgan Kaufmann Publishers Inc
    • Teng, C. M. (1999). Correcting noisy data. In Proceedings of the sixteenth international conference on machine learning, ICML ’99 (pp. 239–248). San Francisco, CA: Morgan Kaufmann Publishers Inc.
  • 35
    • 84893349437 scopus 로고    scopus 로고
    • Wagstaff, K. L., Lanza, N. L., Thompson, D. R., Dietterich, T. G., & Gilmore, M. S. (2013). Guiding scientific discovery with explanations using demud. In AAAI conference on artificial intelligence
    • Wagstaff, K. L., Lanza, N. L., Thompson, D. R., Dietterich, T. G., & Gilmore, M. S. (2013). Guiding scientific discovery with explanations using demud. In AAAI conference on artificial intelligence. http://www.aaai.org/ocs/index.php/AAAI/AAAI13/paper/view/6171.
  • 36
    • 33750691986 scopus 로고    scopus 로고
    • Xu, L., Crammer, K., & Schuurmans, D. (2006). Robust support vector machine training via convex outlier ablation. In Proceedings of the 21st national conference on artificial intelligence, AAAI ’06 (Vol. 1, pp. 536–542). Boston, MA: AAAI Press
    • Xu, L., Crammer, K., & Schuurmans, D. (2006). Robust support vector machine training via convex outlier ablation. In Proceedings of the 21st national conference on artificial intelligence, AAAI ’06 (Vol. 1, pp. 536–542). Boston, MA: AAAI Press. http://dl.acm.org/citation.cfm?id=1597538.1597625.
  • 37
    • 0035788911 scopus 로고    scopus 로고
    • Yamanishi, K., & Takeuchi, J.i. (2001). Discovering outlier filtering rules from unlabeled data: Combining a supervised learner with an unsupervised learner. In 7th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 389–394). ACM
    • Yamanishi, K., & Takeuchi, J.i. (2001). Discovering outlier filtering rules from unlabeled data: Combining a supervised learner with an unsupervised learner. In 7th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 389–394). ACM.
  • 38
    • 85015249301 scopus 로고    scopus 로고
    • Zimek, A., Gaudet, M., Campello, R. J., & Sander, J. (2013). Subsampling for efficient and effective unsupervised outlier detection ensembles. In 19th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 428–436). ACM
    • Zimek, A., Gaudet, M., Campello, R. J., & Sander, J. (2013). Subsampling for efficient and effective unsupervised outlier detection ensembles. In 19th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 428–436). ACM.


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.