-
1
-
-
33749539634
-
Outlier detection by active learning
-
KDD 2006: Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
-
ABE, N., ZADROZNY, B., AND LANGFORD, J. 2006. Outlier detection by active learning. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and DataMining. ACM, 504-509. (Pubitemid 44535546)
-
(2006)
Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
, vol.2006
, pp. 504-509
-
-
Abe, N.1
Zadrozny, B.2
Langford, J.3
-
2
-
-
67149118905
-
Dolphin: An efficient algorithm for mining distance-based outliers in very large datasets
-
ANGIULLI, F. AND FASSETTI, F. 2009. Dolphin: An efficient algorithm for mining distance-based outliers in very large datasets. ACM Trans. Knowl. Discov. Data 3, 1, 1-57.
-
(2009)
ACM Trans. Knowl. Discov. Data 3
, vol.1
, pp. 1-57
-
-
Angiulli, F.1
Fassetti, F.2
-
5
-
-
0035478854
-
Random forests
-
DOI 10.1023/A:1010933404324
-
BREIMAN, L. 2001. Random forests. Mach. Learn. 45, 1, 5-32. (Pubitemid 32933532)
-
(2001)
Machine Learning
, vol.45
, Issue.1
, pp. 5-32
-
-
Breiman, L.1
-
6
-
-
0039253819
-
LOF: Identifying density-based local outliers
-
BREUNIG, M. M., KRIEGEL, H.-P., NG, R. T., AND 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
T, N.G.R.3
Sander, J.4
-
7
-
-
70349598845
-
Appearance-based object recognition using SVMs: Which kernel should i use?
-
CAPUTO, B., SIM, K., FURESJO, F., AND SMOLA, A. 2002. Appearance-based object recognition using SVMs: Which kernel should I use? In Proceedings of the NIPS Workshop on Statitsical Methods for Computational Experiments in Visual Processing and Computer Vision.
-
(2002)
Proceedings of the NIPS Workshop on Statitsical Methods for Computational Experiments in Visual Processing and Computer Vision
-
-
Caputo, B.1
Sim, K.2
Furesjo, F.3
Smola, A.4
-
8
-
-
68049121093
-
Anomaly detection: A survey
-
CHANDOLA, V., BANERJEE, A., AND KUMAR, V. 2009. Anomaly detection: A survey. ACM Comput. Surv. 41, 3, 1-58.
-
(2009)
ACM Comput. Surv.
, vol.41
, Issue.3
, pp. 1-58
-
-
Chandola, V.1
Banerjee, A.2
Kumar, V.3
-
9
-
-
33749478022
-
Very fast outlier detection in large multidimensional datasets
-
ACM
-
CHAUDHARY, A., SZALAY, A. S., SZALAY, E. S., AND MOORE, A. W. 2002. Very fast outlier detection in large multidimensional datasets. In Proceedings of the ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery. ACM.
-
(2002)
Proceedings of the ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery
-
-
Chaudhary, A.1
Szalay, A.S.2
Szalay, E.S.3
Moore, A.W.4
-
10
-
-
33745800069
-
A nonparametric outlier detection for effectively discovering top-N outliers from engineering data
-
Advances in Knowledge Discovery and Data Mining - 10th Pacific-Asia Conference, PAKDD 2006, Proceedings LNAI
-
FAN, H., ZAÏANE, O. R., FOSS, A., AND WU, J. 2006. A nonparametric outlier detection for effectively discovering top-n outliers from engineering data. In Proceedings of the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD). Lecture Notes in Computer Science, vol. 3918, Springer, 557-566. (Pubitemid 44019459)
-
(2006)
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
, vol.3918
, pp. 557-566
-
-
Fan, H.1
Zaiane, O.R.2
Foss, A.3
Wu, J.4
-
11
-
-
19544370003
-
LOADED: Link-based outlier and anomaly detection in evolving data sets
-
Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004
-
GHOTING, A., OTEY, M. E., AND PARTHASARATHY, S. 2004. Loaded: Link-based outlier and anomaly detection in evolving datasets. In Proceedings of the 4th IEEE International Conference on Data Mining (ICDM'04). IEEE Computer Society Press, 387-390. (Pubitemid 40731064)
-
(2004)
Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004
, pp. 387-390
-
-
Ghoting, A.1
Otey, M.E.2
Parthasarathy, S.3
-
12
-
-
0003562954
-
A simple generalisation of the area under the ROC curve for multiple class classification problems
-
DOI 10.1023/A:1010920819831
-
HAND, D. J. AND TILL, R. J. 2001. A simple generalisation of the area under the roc curve for multiple class classification problems. Mach. Learn. 45, 2, 171-186. (Pubitemid 33635984)
-
(2001)
Machine Learning
, vol.45
, Issue.2
, pp. 171-186
-
-
Hand, D.J.1
Till, R.J.2
-
13
-
-
0037410488
-
Discovering cluster-based local outliers
-
HE, Z., XU, X., AND DENG, S. 2003. Discovering cluster-based local outliers. Patt. Recog. Lett. 24, 9-10, 1641-1650.
-
(2003)
Patt. Recog. Lett.
, vol.24
, Issue.9-10
, pp. 1641-1650
-
-
Z, H.E.1
X, X.U.2
Deng, S.3
-
14
-
-
33646512600
-
A unified subspace outlier ensemble framework for outlier detection
-
W. Fan, Z. Wu, and J. Yang Eds., Lecture Notes in Computer Science, Springer
-
HE, Z., DENG, S., AND XU, X. 2005. A unified subspace outlier ensemble framework for outlier detection. In Proceedings of the International Conference on Web-Age Information Management (WAIM). W. Fan, Z. Wu, and J. Yang Eds., Lecture Notes in Computer Science, vol. 3739, Springer, 632-637.
-
(2005)
Proceedings of the International Conference on Web-Age Information Management (WAIM)
, vol.3739
, pp. 632-637
-
-
Z, H.E.1
Deng, S.2
X, X.U.3
-
17
-
-
0034133513
-
Distance-based outliers: Algorithms and applications
-
KNORR, E. M., NG, R. T., AND TUCAKOV, V. 2000. Distance-based outliers: Algorithms and applications. VLDB J. 8, 3-4, 237-253.
-
(2000)
VLDB J.
, vol.8
, Issue.3-4
, pp. 237-253
-
-
Knorr, E.M.1
T, N.G.R.2
Tucakov, V.3
-
20
-
-
67049142378
-
Isolation forest
-
IEEE Computer Society Press
-
LIU, F. T., TING, K. M., AND ZHOU, Z.-H. 2008a. Isolation forest. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM'08). IEEE Computer Society Press, 413-422.
-
(2008)
Proceedings of the 8th IEEE International Conference on Data Mining (ICDM'08).
, pp. 413-422
-
-
Liu, F.T.1
Ting, K.M.2
Zhou, Z.-H.3
-
21
-
-
52249099075
-
Spectrum of variable-random trees
-
LIU, F. T., TING, K. M., AND ZHOU, Z.-H. 2008b. Spectrum of variable-random trees. J. Artif. Intel. Res. 32, 355-384.
-
(2008)
J. Artif. Intel. Res.
, vol.32
, pp. 355-384
-
-
Liu, F.T.1
Ting, K.M.2
Zhou, Z.-H.3
-
22
-
-
84859410618
-
-
Tech. rep. TR2010/2, Monash University
-
LIU, F. T., TING, K. M., AND ZHOU, Z.-H. 2010a. Can isolation-based anomaly detectors handle arbitrary multi-modal patterns in data? Tech. rep. TR2010/2, Monash University.
-
(2010)
Can Isolation-based Anomaly Detectors Handle Arbitrary Multi-modal Patterns in Data?
-
-
Liu, F.T.1
Ting, K.M.2
Zhou, Z.-H.3
-
24
-
-
0033147626
-
Multivariate analysis by data depth: Descriptive statistics, graphics and inference
-
LIU, R. Y., PARELIUS, J. M., AND SINGH, K. 1999. Multivariate analysis by data depth: Descriptive statistics, graphics and inference. Ann. Stat. 27, 3, 783-840.
-
(1999)
Ann. Stat.
, vol.27
, Issue.3
, pp. 783-840
-
-
Liu, R.Y.1
Parelius, J.M.2
Singh, K.3
-
26
-
-
0345359208
-
Loci: Fast outlier detection using the local correlation integral
-
PAPADIMITRIOU, S., KITAGAWA, H., GIBBONS, P., AND FALOUTSOS, C. 2003. Loci: Fast outlier detection using the local correlation integral. In Proceedings of the 19th International Conference on Data Engineering. 315-326.
-
(2003)
Proceedings of the 19th International Conference on Data Engineering.
, pp. 315-326
-
-
Papadimitriou, S.1
Kitagawa, H.2
Gibbons, P.3
Faloutsos, C.4
-
29
-
-
0039845384
-
Efficient algorithms for mining outliers from large datasets
-
ACM
-
RAMASWAMY, S., RASTOGI, R., AND SHIM, K. 2000. Efficient algorithms for mining outliers from large datasets. In Proceedings of the ACM SIGMOD International Conference on Management of Data. ACM, 427-438.
-
(2000)
Proceedings of the ACM SIGMOD International Conference on Management of Data
, pp. 427-438
-
-
Ramaswamy, S.1
Rastogi, R.2
Shim, K.3
-
30
-
-
0030344143
-
Identification of outliers in multivariate data
-
ROCKE, D. M. AND WOODRUFF, D. L. 1996. Identification of outliers in multivariate data. J. Amer. Statist. Soc. 91, 435, 1047-1061.
-
(1996)
J. Amer. Statist. Soc.
, vol.91
, Issue.435
, pp. 1047-1061
-
-
Rocke, D.M.1
Woodruff, D.L.2
-
32
-
-
0344002025
-
On the average shape of binary trees
-
RUSKEY, F. 1980. On the average shape of binary trees. SIAM J. Alg. Disc. Meth. 1, 1, 43-50.
-
(1980)
SIAM J. Alg. Disc. Meth.
, vol.1
, Issue.1
, pp. 43-50
-
-
Ruskey, F.1
-
33
-
-
0000487102
-
Estimating the support of a high-dimensional distribution
-
DOI 10.1162/089976601750264965
-
SCH ÖLKOPF, B., PLATT, J. C., SHAWE-TAYLOR, J. C., SMOLA, A. J., AND WILLIAMSON, R. C. 2001. Estimating the support of a high-dimensional distribution. Neural Comput. 13, 7, 1443-1471. (Pubitemid 33595028)
-
(2001)
Neural Computation
, vol.13
, Issue.7
, pp. 1443-1471
-
-
Scholkopf, B.1
Platt, J.C.2
Shawe-Taylor, J.3
Smola, A.J.4
Williamson, R.C.5
-
34
-
-
33645573203
-
Unsupervised learning with random forest predictors
-
DOI 10.1198/106186006X94072
-
SHI, T. AND HORVATH, S. 2006. Unsupervised learning with random forest predictors. J. Computat. Graph. Stat. 15, 1, 118-138. (Pubitemid 43514414)
-
(2006)
Journal of Computational and Graphical Statistics
, vol.15
, Issue.1
, pp. 118-138
-
-
Shi, T.1
Horvath, S.2
-
35
-
-
27544491192
-
ROCR: Visualizing classifier performance in R
-
DOI 10.1093/bioinformatics/bti623
-
SING, T., SANDER, O., BEERENWINKEL, N., AND LENGAUER, T. 2005. ROCR: Visualizing classifier performance in r. Bioinformatics 21, 20, 3940-3941. (Pubitemid 41535515)
-
(2005)
Bioinformatics
, vol.21
, Issue.20
, pp. 3940-3941
-
-
Sing, T.1
Sander, O.2
Beerenwinkel, N.3
Lengauer, T.4
-
36
-
-
33947697162
-
Conditional anomaly detection
-
SONG, X., WU, M., JERMAINE, C., AND RANKA, S. 2007. Conditional anomaly detection. IEEE Trans. Knowl. Data Eng. 19, 5, 631-645.
-
(2007)
IEEE Trans. Knowl. Data Eng.
, vol.19
, Issue.5
, pp. 631-645
-
-
Song, X.1
M, W.U.2
Jermaine, C.3
Ranka, S.4
-
38
-
-
84945281435
-
Enhancing effectiveness of outlier detections for low density patterns
-
Springer-Verlag
-
TANG, J., CHEN, Z., FU, A. W.-C., AND CHEUNG, D. W.-L. 2002. Enhancing effectiveness of outlier detections for low density patterns. In Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining (PAKDD). Springer-Verlag, 535-548.
-
(2002)
Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining (PAKDD)
, pp. 535-548
-
-
Tang, J.1
Chen, Z.2
Fu, A.W.-C.3
Cheung, D.W.-L.4
-
39
-
-
33749567862
-
Mining distance-based outliers from large databases in any metric space
-
KDD 2006: Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
-
TAO, Y., XIAO, X., AND ZHOU, S. 2006. Mining distance-based outliers from large databases in any metric space. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). ACM, New York, 394-403. (Pubitemid 44535536)
-
(2006)
Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
, vol.2006
, pp. 394-403
-
-
Tao, Y.1
Xiao, X.2
Zhou, S.3
-
40
-
-
0942266514
-
Support vector data description
-
TAX, D. M. J. AND DUIN, R. P. W. 2004. Support vector data description. Mach. Learn. 54, 1, 45-66.
-
(2004)
Mach. Learn.
, vol.54
, Issue.1
, pp. 45-66
-
-
Tax, D.M.J.1
Duin, R.P.W.2
-
42
-
-
27144452309
-
A comparative study of rnn for outlier detection in data mining
-
IEEE Computer Society Press
-
WILLIAMS, G., BAXTER, R., HE, H., HAWKINS, S., AND GU, L. 2002. A comparative study of rnn for outlier detection in data mining. In Proceedings of the IEEE International Conference on Data Mining (ICDM'02). IEEE Computer Society Press, 709-712.
-
(2002)
Proceedings of the IEEE International Conference on Data Mining (ICDM'02).
, pp. 709-712
-
-
Williams, G.1
Baxter, R.2
H, H.E.3
Hawkins, S.4
L, G.U.5
-
44
-
-
0034592923
-
On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms
-
ACM
-
YAMANISHI, K., TAKEUCHI, J.-I.,WILLIAMS, G., AND MILNE, P. 2000. On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms. In Proceedings of the 6th ACMSIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 320-324.
-
(2000)
Proceedings of the 6th ACMSIGKDD International Conference on Knowledge Discovery and Data Mining
, pp. 320-324
-
-
Yamanishi, K.1
Takeuchi, J.-I.2
Williams, G.3
Milne, P.4
-
45
-
-
77951184519
-
Filtering and refinement: A two-stage approach for efficient and effective anomaly detection
-
IEEE Computer Society Press
-
YU, X., TANG, L. A., AND HAN, J. 2009. Filtering and refinement: A two-stage approach for efficient and effective anomaly detection. In Proceedings of the 9th IEEE International Conference on Data Mining (ICDM). IEEE Computer Society Press, 617-626.
-
(2009)
Proceedings of the 9th IEEE International Conference on Data Mining (ICDM).
, pp. 617-626
-
-
X, Y.U.1
Tang, L.A.2
Han, J.3
|