-
1
-
-
33749539634
-
-
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
-
-
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.
-
-
-
-
4
-
-
84949479246
-
-
Heidelberg: Springe
-
Aggarwal, C. C., Hinneburg, A., & Keim, D. A. (2001). On the surprising behavior of distance metrics in high dimensional space. In J. Van den Bussche & V. Vianu (Eds.), Database theory—ICDT 2001. Lecture notes in computer science (Vol. 1973, pp. 420–434). Berlin, Heidelberg: Springer.
-
(2001)
On the surprising behavior of distance metrics in high dimensional space. In J. Van den Bussche & V. Vianu (Eds.), Database theory—ICDT 2001. Lecture notes in computer science (Vol. 1973, pp. 420–434). Berlin
-
-
Aggarwal, C.C.1
Hinneburg, A.2
Keim, D.A.3
-
5
-
-
0038969998
-
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
-
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
-
9
-
-
79957798213
-
Fast outlier detection in high dimensional spaces. In T. Elomaa, H. Mannila, & H. Toivonen (Eds.)
-
Angiulli, F., & Pizzuti, C. (2002). Fast outlier detection in high dimensional spaces. In T. Elomaa, H. Mannila, & H. Toivonen (Eds.), Principles of data mining and knowledge discovery (Vol. 2431, pp. 15–27). Berlin, Heidelberg: Springer. doi:10.1007/3-540-45681-3_2.
-
(2002)
Principles of data mining and knowledge discovery (Vol. 2431, pp. 15–27). Berlin, Heidelberg: Springer
-
-
Angiulli, F.1
Pizzuti, C.2
-
10
-
-
0003495202
-
-
Wiley, New Yor
-
Barlow, R. E., Bartholomew, D. J., Bremner, J., & Brunk, H. D. (1972). Statistical inference under order restrictions: The theory and application of isotonic regression. New York: Wiley.
-
(1972)
Statistical inference under order restrictions: The theory and application of isotonic regression
-
-
Barlow, R.E.1
Bartholomew, D.J.2
Bremner, J.3
Brunk, H.D.4
-
12
-
-
0039253819
-
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
-
13
-
-
68049121093
-
Anomaly detection: A survey
-
Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys (CSUR), 41(3), 15.
-
(2009)
ACM Computing Surveys (CSUR)
, vol.41
, Issue.3
, pp. 15
-
-
Chandola, V.1
Banerjee, A.2
Kumar, V.3
-
14
-
-
29644438050
-
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
-
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
-
16
-
-
84890768280
-
Systematic construction of anomaly detection benchmarks from real data
-
Emmott, A. F., Das, S., Dietterich, T., Fern, A., & Wong, W. K. (2013). Systematic construction of anomaly detection benchmarks from real data. In ACM SIGKDD workshop on outlier detection and description (pp. 16–21).
-
(2013)
In ACM SIGKDD workshop on outlier detection and description
, pp. 16-21
-
-
Emmott, A.F.1
Das, S.2
Dietterich, T.3
Fern, A.4
Wong, W.K.5
-
17
-
-
84939257716
-
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
-
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
-
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
-
20
-
-
34249307704
-
Fp-outlier: Frequent pattern based outlier detection
-
He, Z., Xu, X., Huang, Z. J., & Deng, S. (2005). Fp-outlier: Frequent pattern based outlier detection. Computer Science and Information Systems/ComSIS, 2(1), 103–118.
-
(2005)
Computer Science and Information Systems/ComSIS
, vol.2
, Issue.1
, pp. 103-118
-
-
He, Z.1
Xu, X.2
Huang, Z.J.3
Deng, S.4
-
21
-
-
7544223741
-
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
-
-
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
-
-
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
-
-
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.
-
-
-
-
25
-
-
84859412430
-
Isolation-based anomaly detection
-
Liu, F. T., Ting, K. M., & Zhou, Z. H. (2012). Isolation-based anomaly detection. ACM Transactions on Knowledge Discovery from Data (TKDD), 6(1), 3.
-
(2012)
ACM Transactions on Knowledge Discovery from Data (TKDD)
, vol.6
, Issue.1
, pp. 3
-
-
Liu, F.T.1
Ting, K.M.2
Zhou, Z.H.3
-
26
-
-
70350238780
-
-
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.
-
-
-
-
27
-
-
77958047461
-
Sorex: Subspace outlier ranking exploration toolkit. In J. Balcázar, F. Bonchi, A. Gionis, & M. Sebag (Eds.)
-
(607–610). Berlin, Heidelberg: Springer
-
Müller, E., Schiffer, M., Gerwert, P., Hannen, M., Jansen, T., & Seidl, T. (2010). Sorex: Subspace outlier ranking exploration toolkit. In J. Balcázar, F. Bonchi, A. Gionis, & M. Sebag (Eds.), Machine learning and knowledge discovery in databases. Lecture notes in computer science (pp. 607–610). Berlin, Heidelberg: Springer. doi:10.1007/978-3-642-15939-8_44.
-
(2010)
Machine learning and knowledge discovery in databases. Lecture notes in computer science
-
-
Müller, E.1
Schiffer, M.2
Gerwert, P.3
Hannen, M.4
Jansen, T.5
Seidl, T.6
-
28
-
-
84910664178
-
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
-
-
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
-
-
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
-
-
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
-
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
-
-
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
-
-
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
-
-
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
-
-
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
-
-
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.
-
-
-
|