异常检测 (Chinese Wikipedia)

Analysis of information sources in references of the Wikipedia article "异常检测" in Chinese language version.

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25doi.org

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2wikipedia.org

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2lmu.de

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2siam.org

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2psu.edu

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1umn.edu

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1whiterose.ac.uk

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1umbc.edu

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1byu.edu

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1nih.gov

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1worldcat.org

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1dtic.mil

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1washington.edu

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byu.edu

axon.cs.byu.edu

Smith, M. R.; Martinez, T. Improving classification accuracy by identifying and removing instances that should be misclassified. The 2011 International Joint Conference on Neural Networks (PDF). 2011: 2690 [2016-09-13]. ISBN 978-1-4244-9635-8. doi:10.1109/IJCNN.2011.6033571. （原始内容存档 (PDF)于2016-11-09）.

doi.org

Chandola, V.; Banerjee, A.; Kumar, V. Anomaly detection: A survey (PDF). ACM Computing Surveys. 2009, 41 (3): 1–58 [2016-09-13]. doi:10.1145/1541880.1541882. （原始内容 (PDF)存档于2021-05-06）.
Hodge, V. J.; Austin, J. A Survey of Outlier Detection Methodologies (PDF). Artificial Intelligence Review. 2004, 22 (2): 85–126 [2016-09-13]. doi:10.1007/s10462-004-4304-y. （原始内容 (PDF)存档于2015-06-22）.
Tomek, Ivan. An Experiment with the Edited Nearest-Neighbor Rule. IEEE Transactions on Systems, Man, and Cybernetics（英语：IEEE Transactions on Systems, Man, and Cybernetics）. 1976, 6 (6): 448–452. doi:10.1109/TSMC.1976.4309523.
Smith, M. R.; Martinez, T. Improving classification accuracy by identifying and removing instances that should be misclassified. The 2011 International Joint Conference on Neural Networks (PDF). 2011: 2690 [2016-09-13]. ISBN 978-1-4244-9635-8. doi:10.1109/IJCNN.2011.6033571. （原始内容存档 (PDF)于2016-11-09）.
Knorr, E. M.; Ng, R. T.; Tucakov, V. Distance-based outliers: Algorithms and applications. The VLDB Journal the International Journal on Very Large Data Bases. 2000, 8 (3–4): 237–253. doi:10.1007/s007780050006.
Ramaswamy, S.; Rastogi, R.; Shim, K. Efficient algorithms for mining outliers from large data sets. Proceedings of the 2000 ACM SIGMOD international conference on Management of data – SIGMOD '00: 427. 2000. ISBN 1-58113-217-4. doi:10.1145/342009.335437.
Angiulli, F.; Pizzuti, C. Fast Outlier Detection in High Dimensional Spaces. Principles of Data Mining and Knowledge Discovery. Lecture Notes in Computer Science 2431: 15. 2002. ISBN 978-3-540-44037-6. doi:10.1007/3-540-45681-3_2.
Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; Sander, J. LOF: Identifying Density-based Local Outliers (PDF). Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data. SIGMOD（英语：SIGMOD）. 2000: 93–104 [2016-09-13]. ISBN 1-58113-217-4. doi:10.1145/335191.335388. （原始内容 (PDF)存档于2015-09-23）.
Schubert, E.; Zimek, A.; Kriegel, H. -P. Local outlier detection reconsidered: A generalized view on locality with applications to spatial, video, and network outlier detection. Data Mining and Knowledge Discovery. 2012, 28: 190–237. doi:10.1007/s10618-012-0300-z.
Kriegel, H. P.; Kröger, P.; Schubert, E.; Zimek, A. Outlier Detection in Axis-Parallel Subspaces of High Dimensional Data. Advances in Knowledge Discovery and Data Mining. Lecture Notes in Computer Science 5476: 831. 2009. ISBN 978-3-642-01306-5. doi:10.1007/978-3-642-01307-2_86.
Kriegel, H. P.; Kroger, P.; Schubert, E.; Zimek, A. Outlier Detection in Arbitrarily Oriented Subspaces. 2012 IEEE 12th International Conference on Data Mining: 379. 2012. ISBN 978-1-4673-4649-8. doi:10.1109/ICDM.2012.21.
Zimek, A.; Schubert, E.; Kriegel, H.-P. A survey on unsupervised outlier detection in high-dimensional numerical data. Statistical Analysis and Data Mining. 2012, 5 (5): 363–387. doi:10.1002/sam.11161.
Schölkopf, B.; Platt, J. C.; Shawe-Taylor, J.; Smola, A. J.; Williamson, R. C. Estimating the Support of a High-Dimensional Distribution. Neural Computation. 2001, 13 (7): 1443–71. PMID 11440593. doi:10.1162/089976601750264965.
Hawkins, Simon; He, Hongxing; Williams, Graham; Baxter, Rohan. Outlier Detection Using Replicator Neural Networks. Data Warehousing and Knowledge Discovery. Lecture Notes in Computer Science 2454. 2002: 170–180. ISBN 978-3-540-44123-6. doi:10.1007/3-540-46145-0_17.
He, Z.; Xu, X.; Deng, S. Discovering cluster-based local outliers. Pattern Recognition Letters. 2003, 24 (9–10): 1641–1650. doi:10.1016/S0167-8655(03)00003-5.
Campello, R. J. G. B.; Moulavi, D.; Zimek, A.; Sander, J. Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection. ACM Transactions on Knowledge Discovery from Data. 2015, 10 (1): 5:1–51. doi:10.1145/2733381.
Lazarevic, A.; Kumar, V. Feature bagging for outlier detection. Proc. 11th ACM SIGKDD international conference on Knowledge Discovery in Data Mining. 2005: 157–166. ISBN 1-59593-135-X. doi:10.1145/1081870.1081891.
Nguyen, H. V.; Ang, H. H.; Gopalkrishnan, V. Mining Outliers with Ensemble of Heterogeneous Detectors on Random Subspaces. Database Systems for Advanced Applications. Lecture Notes in Computer Science 5981: 368. 2010. ISBN 978-3-642-12025-1. doi:10.1007/978-3-642-12026-8_29.
Kriegel, H. P.; Kröger, P.; Schubert, E.; Zimek, A. Interpreting and Unifying Outlier Scores. Proceedings of the 2011 SIAM International Conference on Data Mining: 13–24. 2011 [2016-09-13]. ISBN 978-0-89871-992-5. doi:10.1137/1.9781611972818.2. （原始内容 (PDF)存档于2019-06-12）.
Schubert, E.; Wojdanowski, R.; Zimek, A.; Kriegel, H. P. On Evaluation of Outlier Rankings and Outlier Scores. Proceedings of the 2012 SIAM International Conference on Data Mining: 1047–1058. 2012 [2016-09-13]. ISBN 978-1-61197-232-0. doi:10.1137/1.9781611972825.90. （原始内容 (PDF)存档于2019-06-16）.
Zimek, A.; Campello, R. J. G. B.; Sander, J. R. Ensembles for unsupervised outlier detection. ACM SIGKDD Explorations Newsletter. 2014, 15: 11–22. doi:10.1145/2594473.2594476.
Zimek, A.; Campello, R. J. G. B.; Sander, J. R. Data perturbation for outlier detection ensembles. Proceedings of the 26th International Conference on Scientific and Statistical Database Management – SSDBM '14: 1. 2014. ISBN 978-1-4503-2722-0. doi:10.1145/2618243.2618257.
Campos, Guilherme O.; Zimek, Arthur; Sander, Jörg; Campello, Ricardo J. G. B.; Micenková, Barbora; Schubert, Erich; Assent, Ira; Houle, Michael E. On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study. Data Mining and Knowledge Discovery. 2016, 30 (4): 891. ISSN 1384-5810. doi:10.1007/s10618-015-0444-8.
Denning, D. E. An Intrusion-Detection Model (PDF). IEEE Transactions on Software Engineering. 1987, SE–13 (2): 222–232 [2016-09-13]. doi:10.1109/TSE.1987.232894. CiteSeerX: 10.1.1.102.5127. （原始内容 (PDF)存档于2015-06-22）.
Teng, H. S.; Chen, K.; Lu, S. C. Adaptive real-time anomaly detection using inductively generated sequential patterns (PDF). Proceedings of the IEEE Computer Society Symposium on Research in Security and Privacy. 1990: 278–284. ISBN 0-8186-2060-9. doi:10.1109/RISP.1990.63857. ^{[永久失效連結]}

dtic.mil

Denning, D. E. An Intrusion-Detection Model (PDF). IEEE Transactions on Software Engineering. 1987, SE–13 (2): 222–232 [2016-09-13]. doi:10.1109/TSE.1987.232894. CiteSeerX: 10.1.1.102.5127. （原始内容 (PDF)存档于2015-06-22）.

lmu.de

dbs.ifi.lmu.de

Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; Sander, J. LOF: Identifying Density-based Local Outliers (PDF). Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data. SIGMOD（英语：SIGMOD）. 2000: 93–104 [2016-09-13]. ISBN 1-58113-217-4. doi:10.1145/335191.335388. （原始内容 (PDF)存档于2015-09-23）.
Anomaly detection benchmark data repository （页面存档备份，存于互联网档案馆） of the Ludwig-Maximilians-Universität München; Mirror （页面存档备份，存于互联网档案馆） at University of São Paulo.

nih.gov

ncbi.nlm.nih.gov

Schölkopf, B.; Platt, J. C.; Shawe-Taylor, J.; Smola, A. J.; Williamson, R. C. Estimating the Support of a High-Dimensional Distribution. Neural Computation. 2001, 13 (7): 1443–71. PMID 11440593. doi:10.1162/089976601750264965.

psu.edu

citeseerx.ist.psu.edu

Denning, D. E. An Intrusion-Detection Model (PDF). IEEE Transactions on Software Engineering. 1987, SE–13 (2): 222–232 [2016-09-13]. doi:10.1109/TSE.1987.232894. CiteSeerX: 10.1.1.102.5127. （原始内容 (PDF)存档于2015-06-22）.
Jones, Anita K.; Sielken, Robert S. Computer System Intrusion Detection: A Survey. Technical Report, Department of Computer Science, University of Virginia, Charlottesville, VA. 1999. CiteSeerX: 10.1.1.24.7802.

siam.org

epubs.siam.org

Kriegel, H. P.; Kröger, P.; Schubert, E.; Zimek, A. Interpreting and Unifying Outlier Scores. Proceedings of the 2011 SIAM International Conference on Data Mining: 13–24. 2011 [2016-09-13]. ISBN 978-0-89871-992-5. doi:10.1137/1.9781611972818.2. （原始内容 (PDF)存档于2019-06-12）.
Schubert, E.; Wojdanowski, R.; Zimek, A.; Kriegel, H. P. On Evaluation of Outlier Rankings and Outlier Scores. Proceedings of the 2012 SIAM International Conference on Data Mining: 1047–1058. 2012 [2016-09-13]. ISBN 978-1-61197-232-0. doi:10.1137/1.9781611972825.90. （原始内容 (PDF)存档于2019-06-16）.

umbc.edu

csee.umbc.edu

Dokas, Paul; Ertoz, Levent; Kumar, Vipin; Lazarevic, Aleksandar; Srivastava, Jaideep; Tan, Pang-Ning. Data mining for network intrusion detection (PDF). Proceedings NSF Workshop on Next Generation Data Mining. 2002 [2016-09-13]. （原始内容 (PDF)存档于2015-09-23）.

umn.edu

bsi.umn.edu

Chandola, V.; Banerjee, A.; Kumar, V. Anomaly detection: A survey (PDF). ACM Computing Surveys. 2009, 41 (3): 1–58 [2016-09-13]. doi:10.1145/1541880.1541882. （原始内容 (PDF)存档于2021-05-06）.

usp.br

lapad-web.icmc.usp.br

Anomaly detection benchmark data repository （页面存档备份，存于互联网档案馆） of the Ludwig-Maximilians-Universität München; Mirror （页面存档备份，存于互联网档案馆） at University of São Paulo.

washington.edu

cs.washington.edu

Teng, H. S.; Chen, K.; Lu, S. C. Adaptive real-time anomaly detection using inductively generated sequential patterns (PDF). Proceedings of the IEEE Computer Society Symposium on Research in Security and Privacy. 1990: 278–284. ISBN 0-8186-2060-9. doi:10.1109/RISP.1990.63857. ^{[永久失效連結]}

web.archive.org

Chandola, V.; Banerjee, A.; Kumar, V. Anomaly detection: A survey (PDF). ACM Computing Surveys. 2009, 41 (3): 1–58 [2016-09-13]. doi:10.1145/1541880.1541882. （原始内容 (PDF)存档于2021-05-06）.
Hodge, V. J.; Austin, J. A Survey of Outlier Detection Methodologies (PDF). Artificial Intelligence Review. 2004, 22 (2): 85–126 [2016-09-13]. doi:10.1007/s10462-004-4304-y. （原始内容 (PDF)存档于2015-06-22）.
Dokas, Paul; Ertoz, Levent; Kumar, Vipin; Lazarevic, Aleksandar; Srivastava, Jaideep; Tan, Pang-Ning. Data mining for network intrusion detection (PDF). Proceedings NSF Workshop on Next Generation Data Mining. 2002 [2016-09-13]. （原始内容 (PDF)存档于2015-09-23）.
Smith, M. R.; Martinez, T. Improving classification accuracy by identifying and removing instances that should be misclassified. The 2011 International Joint Conference on Neural Networks (PDF). 2011: 2690 [2016-09-13]. ISBN 978-1-4244-9635-8. doi:10.1109/IJCNN.2011.6033571. （原始内容存档 (PDF)于2016-11-09）.
Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; Sander, J. LOF: Identifying Density-based Local Outliers (PDF). Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data. SIGMOD（英语：SIGMOD）. 2000: 93–104 [2016-09-13]. ISBN 1-58113-217-4. doi:10.1145/335191.335388. （原始内容 (PDF)存档于2015-09-23）.
Kriegel, H. P.; Kröger, P.; Schubert, E.; Zimek, A. Interpreting and Unifying Outlier Scores. Proceedings of the 2011 SIAM International Conference on Data Mining: 13–24. 2011 [2016-09-13]. ISBN 978-0-89871-992-5. doi:10.1137/1.9781611972818.2. （原始内容 (PDF)存档于2019-06-12）.
Schubert, E.; Wojdanowski, R.; Zimek, A.; Kriegel, H. P. On Evaluation of Outlier Rankings and Outlier Scores. Proceedings of the 2012 SIAM International Conference on Data Mining: 1047–1058. 2012 [2016-09-13]. ISBN 978-1-61197-232-0. doi:10.1137/1.9781611972825.90. （原始内容 (PDF)存档于2019-06-16）.
Anomaly detection benchmark data repository （页面存档备份，存于互联网档案馆） of the Ludwig-Maximilians-Universität München; Mirror （页面存档备份，存于互联网档案馆） at University of São Paulo.
Denning, D. E. An Intrusion-Detection Model (PDF). IEEE Transactions on Software Engineering. 1987, SE–13 (2): 222–232 [2016-09-13]. doi:10.1109/TSE.1987.232894. CiteSeerX: 10.1.1.102.5127. （原始内容 (PDF)存档于2015-06-22）.

whiterose.ac.uk

eprints.whiterose.ac.uk

Hodge, V. J.; Austin, J. A Survey of Outlier Detection Methodologies (PDF). Artificial Intelligence Review. 2004, 22 (2): 85–126 [2016-09-13]. doi:10.1007/s10462-004-4304-y. （原始内容 (PDF)存档于2015-06-22）.

wikipedia.org

en.wikipedia.org

Tomek, Ivan. An Experiment with the Edited Nearest-Neighbor Rule. IEEE Transactions on Systems, Man, and Cybernetics（英语：IEEE Transactions on Systems, Man, and Cybernetics）. 1976, 6 (6): 448–452. doi:10.1109/TSMC.1976.4309523.
Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; Sander, J. LOF: Identifying Density-based Local Outliers (PDF). Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data. SIGMOD（英语：SIGMOD）. 2000: 93–104 [2016-09-13]. ISBN 1-58113-217-4. doi:10.1145/335191.335388. （原始内容 (PDF)存档于2015-09-23）.

worldcat.org

Campos, Guilherme O.; Zimek, Arthur; Sander, Jörg; Campello, Ricardo J. G. B.; Micenková, Barbora; Schubert, Erich; Assent, Ira; Houle, Michael E. On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study. Data Mining and Knowledge Discovery. 2016, 30 (4): 891. ISSN 1384-5810. doi:10.1007/s10618-015-0444-8.