Détection d'anomalies (French Wikipedia)

Analysis of information sources in references of the Wikipedia article "Détection d'anomalies" in French language version.

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books.google.com

Simon Hawkins, Hongxing He, Graham Williams et Rohan Baxter, Data Warehousing and Knowledge Discovery, vol. 2454, coll. « Lecture Notes in Computer Science », 2002, 337 p. (ISBN 978-3-540-44123-6, DOI 10.1007/3-540-46145-0_17, lire en ligne), « Outlier Detection Using Replicator Neural Networks »

byu.edu

axon.cs.byu.edu

M. R. Smith et T. Martinez, The 2011 International Joint Conference on Neural Networks, 2011, 2690 p. (ISBN 978-1-4244-9635-8, DOI 10.1109/IJCNN.2011.6033571, lire en ligne), « Improving classification accuracy by identifying and removing instances that should be misclassified »

computer.org

(en) Fei Tony Liu, Kai Ming Ting et Zhi-Hua Zhou, Isolation Forest (2008 Eighth IEEE International Conference on Data Mining), décembre 2008, 413–422 p. (ISBN 978-0-7695-3502-9, DOI 10.1109/ICDM.2008.17, lire en ligne)

doi.org

dx.doi.org

Arthur Zimek et Erich Schubert, Encyclopedia of Database Systems, Springer New York, 2017, 1–5 p. (ISBN 978-1-4899-7993-3, DOI 10.1007/978-1-4899-7993-3_80719-1), « Outlier Detection »
V. J. Hodge et J. Austin, « A Survey of Outlier Detection Methodologies », Artificial Intelligence Review, vol. 22, n^o 2,‎ 2004, p. 85–126 (DOI 10.1007/s10462-004-4304-y, CiteSeer^x 10.1.1.318.4023, lire en ligne)
V. Chandola, A. Banerjee et V. Kumar, « Anomaly detection: A survey », ACM Computing Surveys, vol. 41, n^o 3,‎ 2009, p. 1–58 (DOI 10.1145/1541880.1541882)
Ivan Tomek, « An Experiment with the Edited Nearest-Neighbor Rule », IEEE Transactions on Systems, Man, and Cybernetics, vol. 6, n^o 6,‎ 1976, p. 448–452 (DOI 10.1109/TSMC.1976.4309523)
M. R. Smith et T. Martinez, The 2011 International Joint Conference on Neural Networks, 2011, 2690 p. (ISBN 978-1-4244-9635-8, DOI 10.1109/IJCNN.2011.6033571, lire en ligne), « Improving classification accuracy by identifying and removing instances that should be misclassified »
Arthur Zimek et Peter Filzmoser, « There and back again: Outlier detection between statistical reasoning and data mining algorithms », Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 8, n^o 6,‎ 2018, e1280 (ISSN 1942-4787, DOI 10.1002/widm.1280)
E. M. Knorr, R. T. Ng et V. Tucakov, « Distance-based outliers: Algorithms and applications », The VLDB Journal the International Journal on Very Large Data Bases, vol. 8, n^os 3–4,‎ 2000, p. 237–253 (DOI 10.1007/s007780050006, CiteSeer^x 10.1.1.43.1842)
S. Ramaswamy, R. Rastogi et K. Shim « Efficient algorithms for mining outliers from large data sets » (2000) (DOI 10.1145/342009.335437)
—Proceedings of the 2000 ACM SIGMOD international conference on Management of data – SIGMOD '00
F. Angiulli et C. Pizzuti « Fast Outlier Detection in High Dimensional Spaces » (2002) (DOI 10.1007/3-540-45681-3_2)
—Principles of Data Mining and Knowledge Discovery
M. M. Breunig, H.-P. Kriegel, R. T. Ng et J. Sander « LOF: Identifying Density-based Local Outliers » (2000) (DOI 10.1145/335191.335388, lire en ligne)
— « (ibid.) », Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data,‎ 2000, p. 93–104
(en) Fei Tony Liu, Kai Ming Ting et Zhi-Hua Zhou, Isolation Forest (2008 Eighth IEEE International Conference on Data Mining), décembre 2008, 413–422 p. (ISBN 978-0-7695-3502-9, DOI 10.1109/ICDM.2008.17, lire en ligne)
E. Schubert, A. Zimek et H. -P. Kriegel, « Local outlier detection reconsidered: A generalized view on locality with applications to spatial, video, and network outlier detection », Data Mining and Knowledge Discovery, vol. 28,‎ 2012, p. 190–237 (DOI 10.1007/s10618-012-0300-z)
H. P. Kriegel, P. Kröger, E. Schubert et A. Zimek « Outlier Detection in Axis-Parallel Subspaces of High Dimensional Data » (2009) (DOI 10.1007/978-3-642-01307-2_86)
—Advances in Knowledge Discovery and Data Mining
H. P. Kriegel, P. Kroger, E. Schubert et A. Zimek « Outlier Detection in Arbitrarily Oriented Subspaces » (2012) (DOI 10.1109/ICDM.2012.21)
—2012 IEEE 12th International Conference on Data Mining
H. Fanaee-T et J. Gama, « Tensor-based anomaly detection: An interdisciplinary survey », Knowledge-Based Systems, vol. 98,‎ 2016, p. 130–147 (DOI 10.1016/j.knosys.2016.01.027)
A. Zimek, E. Schubert et H.-P. Kriegel, « A survey on unsupervised outlier detection in high-dimensional numerical data », Statistical Analysis and Data Mining, vol. 5, n^o 5,‎ 2012, p. 363–387 (DOI 10.1002/sam.11161)
B. Schölkopf, J. C. Platt, J. Shawe-Taylor, A. J. Smola et R. C. Williamson, « Estimating the Support of a High-Dimensional Distribution », Neural Computation, vol. 13, n^o 7,‎ 2001, p. 1443–71 (PMID 11440593, DOI 10.1162/089976601750264965, CiteSeer^x 10.1.1.4.4106)
Simon Hawkins, Hongxing He, Graham Williams et Rohan Baxter, Data Warehousing and Knowledge Discovery, vol. 2454, coll. « Lecture Notes in Computer Science », 2002, 337 p. (ISBN 978-3-540-44123-6, DOI 10.1007/3-540-46145-0_17, lire en ligne), « Outlier Detection Using Replicator Neural Networks »
Z. He, X. Xu et S. Deng, « Discovering cluster-based local outliers », Pattern Recognition Letters, vol. 24, n^os 9–10,‎ 2003, p. 1641–1650 (DOI 10.1016/S0167-8655(03)00003-5, CiteSeer^x 10.1.1.20.4242)
R. J. G. B. Campello, D. Moulavi, A. Zimek et J. Sander, « Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection », ACM Transactions on Knowledge Discovery from Data, vol. 10, n^o 1,‎ 2015, p. 5:1–51 (DOI 10.1145/2733381)
A. Lazarevic et V. Kumar, Feature bagging for outlier detection (Proc. 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining), 2005, 157–166 p. (ISBN 978-1-59593-135-1, DOI 10.1145/1081870.1081891)
H. V. Nguyen, H. H. Ang et V. Gopalkrishnan « Mining Outliers with Ensemble of Heterogeneous Detectors on Random Subspaces » (2010) (DOI 10.1007/978-3-642-12026-8_29)
—Database Systems for Advanced Applications
H. P. Kriegel, P. Kröger, E. Schubert et A. Zimek « Interpreting and Unifying Outlier Scores » (2011) (DOI 10.1137/1.9781611972818.2, CiteSeer^x 10.1.1.232.2719)
—Proceedings of the 2011 SIAM International Conference on Data Mining
E. Schubert, R. Wojdanowski, A. Zimek et H. P. Kriegel « On Evaluation of Outlier Rankings and Outlier Scores » (2012) (DOI 10.1137/1.9781611972825.90)
—Proceedings of the 2012 SIAM International Conference on Data Mining
A. Zimek, R. J. G. B. Campello et J. R. Sander, « Ensembles for unsupervised outlier detection », ACM SIGKDD Explorations Newsletter, vol. 15,‎ 2014, p. 11–22 (DOI 10.1145/2594473.2594476)
A. Zimek, R. J. G. B. Campello et J. R. Sander « Data perturbation for outlier detection ensembles » (2014) (DOI 10.1145/2618243.2618257)
—Proceedings of the 26th International Conference on Scientific and Statistical Database Management – SSDBM '14
Guilherme O. Campos, Arthur Zimek, Jörg Sander, Ricardo J. G. B. Campello, Barbora Micenková, Erich Schubert, Ira Assent et Michael E. Houle, « On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study », Data Mining and Knowledge Discovery, vol. 30, n^o 4,‎ 2016, p. 891 (ISSN 1384-5810, DOI 10.1007/s10618-015-0444-8)
D. E. Denning, « An Intrusion-Detection Model », IEEE Transactions on Software Engineering, vol. SE-13, n^o 2,‎ 1987, p. 222–232 (DOI 10.1109/TSE.1987.232894, CiteSeer^x 10.1.1.102.5127, lire en ligne)
H. S. Teng, K. Chen et S. C. Lu, Adaptive real-time anomaly detection using inductively generated sequential patterns (Proceedings of the IEEE Computer Society Symposium on Research in Security and Privacy), 1990, 401 p. (ISBN 978-0-8186-2060-7, DOI 10.1109/RISP.1990.63857, lire en ligne)

dtic.mil

D. E. Denning, « An Intrusion-Detection Model », IEEE Transactions on Software Engineering, vol. SE-13, n^o 2,‎ 1987, p. 222–232 (DOI 10.1109/TSE.1987.232894, CiteSeer^x 10.1.1.102.5127, lire en ligne)

issn.org

portal.issn.org

Arthur Zimek et Peter Filzmoser, « There and back again: Outlier detection between statistical reasoning and data mining algorithms », Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 8, n^o 6,‎ 2018, e1280 (ISSN 1942-4787, DOI 10.1002/widm.1280)
Guilherme O. Campos, Arthur Zimek, Jörg Sander, Ricardo J. G. B. Campello, Barbora Micenková, Erich Schubert, Ira Assent et Michael E. Houle, « On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study », Data Mining and Knowledge Discovery, vol. 30, n^o 4,‎ 2016, p. 891 (ISSN 1384-5810, DOI 10.1007/s10618-015-0444-8)

lmu.de

dbs.ifi.lmu.de

M. M. Breunig, H.-P. Kriegel, R. T. Ng et J. Sander « LOF: Identifying Density-based Local Outliers » (2000) (DOI 10.1145/335191.335388, lire en ligne)
— « (ibid.) », Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data,‎ 2000, p. 93–104
Anomaly detection benchmark data repository of the Ludwig-Maximilians-Universität München; Mirror at University of São Paulo.

nih.gov

ncbi.nlm.nih.gov

B. Schölkopf, J. C. Platt, J. Shawe-Taylor, A. J. Smola et R. C. Williamson, « Estimating the Support of a High-Dimensional Distribution », Neural Computation, vol. 13, n^o 7,‎ 2001, p. 1443–71 (PMID 11440593, DOI 10.1162/089976601750264965, CiteSeer^x 10.1.1.4.4106)

psu.edu

citeseerx.ist.psu.edu

V. J. Hodge et J. Austin, « A Survey of Outlier Detection Methodologies », Artificial Intelligence Review, vol. 22, n^o 2,‎ 2004, p. 85–126 (DOI 10.1007/s10462-004-4304-y, CiteSeer^x 10.1.1.318.4023, lire en ligne)
E. M. Knorr, R. T. Ng et V. Tucakov, « Distance-based outliers: Algorithms and applications », The VLDB Journal the International Journal on Very Large Data Bases, vol. 8, n^os 3–4,‎ 2000, p. 237–253 (DOI 10.1007/s007780050006, CiteSeer^x 10.1.1.43.1842)
B. Schölkopf, J. C. Platt, J. Shawe-Taylor, A. J. Smola et R. C. Williamson, « Estimating the Support of a High-Dimensional Distribution », Neural Computation, vol. 13, n^o 7,‎ 2001, p. 1443–71 (PMID 11440593, DOI 10.1162/089976601750264965, CiteSeer^x 10.1.1.4.4106)
Z. He, X. Xu et S. Deng, « Discovering cluster-based local outliers », Pattern Recognition Letters, vol. 24, n^os 9–10,‎ 2003, p. 1641–1650 (DOI 10.1016/S0167-8655(03)00003-5, CiteSeer^x 10.1.1.20.4242)
H. P. Kriegel, P. Kröger, E. Schubert et A. Zimek « Interpreting and Unifying Outlier Scores » (2011) (DOI 10.1137/1.9781611972818.2, CiteSeer^x 10.1.1.232.2719)
—Proceedings of the 2011 SIAM International Conference on Data Mining
D. E. Denning, « An Intrusion-Detection Model », IEEE Transactions on Software Engineering, vol. SE-13, n^o 2,‎ 1987, p. 222–232 (DOI 10.1109/TSE.1987.232894, CiteSeer^x 10.1.1.102.5127, lire en ligne)

umbc.edu

csee.umbc.edu

Paul Dokas, Levent Ertoz, Vipin Kumar, Aleksandar Lazarevic, Jaideep Srivastava et Pang-Ning Tan, « Data mining for network intrusion detection », Proceedings NSF Workshop on Next Generation Data Mining,‎ 2002 (lire en ligne)

unc.edu

cs.unc.edu

H. S. Teng, K. Chen et S. C. Lu, Adaptive real-time anomaly detection using inductively generated sequential patterns (Proceedings of the IEEE Computer Society Symposium on Research in Security and Privacy), 1990, 401 p. (ISBN 978-0-8186-2060-7, DOI 10.1109/RISP.1990.63857, lire en ligne)

usp.br

lapad-web.icmc.usp.br

Anomaly detection benchmark data repository of the Ludwig-Maximilians-Universität München; Mirror at University of São Paulo.

whiterose.ac.uk

eprints.whiterose.ac.uk

V. J. Hodge et J. Austin, « A Survey of Outlier Detection Methodologies », Artificial Intelligence Review, vol. 22, n^o 2,‎ 2004, p. 85–126 (DOI 10.1007/s10462-004-4304-y, CiteSeer^x 10.1.1.318.4023, lire en ligne)