Rilevamento delle anomalie (Italian Wikipedia)

Analysis of information sources in references of the Wikipedia article "Rilevamento delle anomalie" in Italian language version.

refsWebsite

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22doi.org

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12oadoi.org

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3worldcat.org

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3unibo.it

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2web.archive.org

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2lmu.de

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1umbc.edu

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1byu.edu

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1unc.edu

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1computer.org

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1nih.gov

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1archive.org

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1usp.br

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archive.org

Simon Hawkins, Hongxing He e Graham Williams, Outlier Detection Using Replicator Neural Networks, in Data Warehousing and Knowledge Discovery, collana Lecture Notes in Computer Science, vol. 2454, 2002, pp. 170–180, DOI:10.1007/3-540-46145-0_17, ISBN 978-3-540-44123-6.

byu.edu

axon.cs.byu.edu

M. R. Smith e T. Martinez, Improving classification accuracy by identifying and removing instances that should be misclassified (PDF), in The 2011 International Joint Conference on Neural Networks, 2011, pp. 2690, DOI:10.1109/IJCNN.2011.6033571, ISBN 978-1-4244-9635-8.

computer.org

(EN) Fei Tony Liu, Kai Ming Ting e Zhi-Hua Zhou, Isolation Forest, in 2008 Eighth IEEE International Conference on Data Mining, dicembre 2008, pp. 413–422, DOI:10.1109/ICDM.2008.17, ISBN 9780769535029.

doi.org

dx.doi.org

Outlier Detection, DOI:10.1007/978-1-4899-7993-3_80719-1, ISBN 9781489979933.
Victoria Hodge e Jim Austin, A Survey of Outlier Detection Methodologies, in Artificial Intelligence Review, vol. 22, 2004, DOI:10.1007/s10462-004-4304-y.
Anomaly detection: A survey, in ACM Computing Surveys, vol. 41, n. 3, 2009, pp. 1–58, DOI:10.1145/1541880.1541882.
Bergmann, The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection, in International Journal of Computer Vision, vol. 129, n. 4, pp. 1038–1059, DOI:10.1007/s11263-020-01400-4, ISSN 0920-5691 (WC · ACNP).
An Experiment with the Edited Nearest-Neighbor Rule, in IEEE Transactions on Systems, Man, and Cybernetics, vol. 6, n. 6, 1976, pp. 448–452, DOI:10.1109/TSMC.1976.4309523.
M. R. Smith e T. Martinez, Improving classification accuracy by identifying and removing instances that should be misclassified (PDF), in The 2011 International Joint Conference on Neural Networks, 2011, pp. 2690, DOI:10.1109/IJCNN.2011.6033571, ISBN 978-1-4244-9635-8.
Dorothy Denning, An Intrusion-Detection Model, in IEEE Transactions on Software Engineering, vol. 13, n. 2, 1987, DOI:10.1109/TSE.1987.232894.
H. S. Teng, K. Chen e S. C. Lu, Adaptive real-time anomaly detection using inductively generated sequential patterns (PDF), in Proceedings of the IEEE Computer Society Symposium on Research in Security and Privacy, 1990, pp. 278–284, DOI:10.1109/RISP.1990.63857, ISBN 978-0-8186-2060-7.
Probabilistic noise identification and data cleaning, in Third IEEE International Conference on Data Mining, IEEE Comput. Soc, 2003, pp. 131–138, DOI:10.1109/icdm.2003.1250912, ISBN 0-7695-1978-4.
There and back again: Outlier detection between statistical reasoning and data mining algorithms (PDF), in Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 8, n. 6, 2018, pp. e1280, DOI:10.1002/widm.1280, ISSN 1942-4787 (WC · ACNP). URL consultato il 19 gennaio 2022 (archiviato dall'url originale il 14 novembre 2021).
Edwin M. Knorr, Raymond T. Ng e Vladimir Tucakov, Distance-based outliers: algorithms and applications, in The VLDB Journal, vol. 8, 2000, DOI:10.1007/s007780050006.
Efficient algorithms for mining outliers from large data sets, Proceedings of the 2000 ACM SIGMOD international conference on Management of data, 2000, DOI:10.1145/342009.335437, ISBN 1-58113-217-4.
Fabrizio Angiulli e Clara Pizzuti, Fast Outlier Detection in High Dimensional Spaces, European Conference on Principles of Data Mining and Knowledge Discovery, vol. 2431, 2002, DOI:10.1007/3-540-45681-3_2, ISBN 978-3-540-44037-6.
LOF: identifying density-based local outliers (PDF), SIGMOD Record, 2000, DOI:10.1145/335191.335388, ISBN 1-58113-217-4.
(EN) Fei Tony Liu, Kai Ming Ting e Zhi-Hua Zhou, Isolation Forest, in 2008 Eighth IEEE International Conference on Data Mining, dicembre 2008, pp. 413–422, DOI:10.1109/ICDM.2008.17, ISBN 9780769535029.
(EN) Isolation-Based Anomaly Detection, in ACM Transactions on Knowledge Discovery from Data, vol. 6, n. 1, DOI:10.1145/2133360.2133363.
Local outlier detection reconsidered: a generalized view on locality with applications to spatial, video, and network outlier detection, in Data Mining and Knowledge Discovery, vol. 28, 2012, DOI:10.1007/s10618-012-0300-z.
Estimating the Support of a High-Dimensional Distribution, in Neural Computation, vol. 13, n. 7, 2001, DOI:10.1162/089976601750264965, PMID 11440593.
Simon Hawkins, Hongxing He e Graham Williams, Outlier Detection Using Replicator Neural Networks, in Data Warehousing and Knowledge Discovery, collana Lecture Notes in Computer Science, vol. 2454, 2002, pp. 170–180, DOI:10.1007/3-540-46145-0_17, ISBN 978-3-540-44123-6.
Discovering cluster-based local outliers, vol. 24, 2003, DOI:10.1016/S0167-8655(03)00003-5.
Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection, vol. 10, 2015, DOI:10.1145/2733381.
On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study, in Data Mining and Knowledge Discovery, vol. 30, 2016, DOI:10.1007/s10618-015-0444-8, ISSN 1384-5810 (WC · ACNP).

lmu.de

dbs.ifi.lmu.de

LOF: identifying density-based local outliers (PDF), SIGMOD Record, 2000, DOI:10.1145/335191.335388, ISBN 1-58113-217-4.
Anomaly detection benchmark data repository della Ludwig-Maximilians-Universität München; Altro collegamento Archiviato il 31 marzo 2022 in Internet Archive. dall'Università di San Paolo.

nih.gov

ncbi.nlm.nih.gov

Estimating the Support of a High-Dimensional Distribution, in Neural Computation, vol. 13, n. 7, 2001, DOI:10.1162/089976601750264965, PMID 11440593.

oadoi.org

Victoria Hodge e Jim Austin, A Survey of Outlier Detection Methodologies, in Artificial Intelligence Review, vol. 22, 2004, DOI:10.1007/s10462-004-4304-y.
Anomaly detection: A survey, in ACM Computing Surveys, vol. 41, n. 3, 2009, pp. 1–58, DOI:10.1145/1541880.1541882.
Bergmann, The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection, in International Journal of Computer Vision, vol. 129, n. 4, pp. 1038–1059, DOI:10.1007/s11263-020-01400-4, ISSN 0920-5691 (WC · ACNP).
An Experiment with the Edited Nearest-Neighbor Rule, in IEEE Transactions on Systems, Man, and Cybernetics, vol. 6, n. 6, 1976, pp. 448–452, DOI:10.1109/TSMC.1976.4309523.
Dorothy Denning, An Intrusion-Detection Model, in IEEE Transactions on Software Engineering, vol. 13, n. 2, 1987, DOI:10.1109/TSE.1987.232894.
Edwin M. Knorr, Raymond T. Ng e Vladimir Tucakov, Distance-based outliers: algorithms and applications, in The VLDB Journal, vol. 8, 2000, DOI:10.1007/s007780050006.
(EN) Isolation-Based Anomaly Detection, in ACM Transactions on Knowledge Discovery from Data, vol. 6, n. 1, DOI:10.1145/2133360.2133363.
Local outlier detection reconsidered: a generalized view on locality with applications to spatial, video, and network outlier detection, in Data Mining and Knowledge Discovery, vol. 28, 2012, DOI:10.1007/s10618-012-0300-z.
Estimating the Support of a High-Dimensional Distribution, in Neural Computation, vol. 13, n. 7, 2001, DOI:10.1162/089976601750264965, PMID 11440593.
Discovering cluster-based local outliers, vol. 24, 2003, DOI:10.1016/S0167-8655(03)00003-5.
Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection, vol. 10, 2015, DOI:10.1145/2733381.
On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study, in Data Mining and Knowledge Discovery, vol. 30, 2016, DOI:10.1007/s10618-015-0444-8, ISSN 1384-5810 (WC · ACNP).

umbc.edu

csee.umbc.edu

Data mining for network intrusion detection (PDF), in Proceedings NSF Workshop on Next Generation Data Mining, 2002.

unc.edu

cs.unc.edu

H. S. Teng, K. Chen e S. C. Lu, Adaptive real-time anomaly detection using inductively generated sequential patterns (PDF), in Proceedings of the IEEE Computer Society Symposium on Research in Security and Privacy, 1990, pp. 278–284, DOI:10.1109/RISP.1990.63857, ISBN 978-0-8186-2060-7.

unibo.it

acnpsearch.unibo.it

Bergmann, The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection, in International Journal of Computer Vision, vol. 129, n. 4, pp. 1038–1059, DOI:10.1007/s11263-020-01400-4, ISSN 0920-5691 (WC · ACNP).
There and back again: Outlier detection between statistical reasoning and data mining algorithms (PDF), in Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 8, n. 6, 2018, pp. e1280, DOI:10.1002/widm.1280, ISSN 1942-4787 (WC · ACNP). URL consultato il 19 gennaio 2022 (archiviato dall'url originale il 14 novembre 2021).
On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study, in Data Mining and Knowledge Discovery, vol. 30, 2016, DOI:10.1007/s10618-015-0444-8, ISSN 1384-5810 (WC · ACNP).

usp.br

lapad-web.icmc.usp.br

Anomaly detection benchmark data repository della Ludwig-Maximilians-Universität München; Altro collegamento Archiviato il 31 marzo 2022 in Internet Archive. dall'Università di San Paolo.

web.archive.org

There and back again: Outlier detection between statistical reasoning and data mining algorithms (PDF), in Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 8, n. 6, 2018, pp. e1280, DOI:10.1002/widm.1280, ISSN 1942-4787 (WC · ACNP). URL consultato il 19 gennaio 2022 (archiviato dall'url originale il 14 novembre 2021).
Anomaly detection benchmark data repository della Ludwig-Maximilians-Universität München; Altro collegamento Archiviato il 31 marzo 2022 in Internet Archive. dall'Università di San Paolo.

worldcat.org

Bergmann, The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection, in International Journal of Computer Vision, vol. 129, n. 4, pp. 1038–1059, DOI:10.1007/s11263-020-01400-4, ISSN 0920-5691 (WC · ACNP).
There and back again: Outlier detection between statistical reasoning and data mining algorithms (PDF), in Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 8, n. 6, 2018, pp. e1280, DOI:10.1002/widm.1280, ISSN 1942-4787 (WC · ACNP). URL consultato il 19 gennaio 2022 (archiviato dall'url originale il 14 novembre 2021).
On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study, in Data Mining and Knowledge Discovery, vol. 30, 2016, DOI:10.1007/s10618-015-0444-8, ISSN 1384-5810 (WC · ACNP).