-
2
-
-
85012236181
-
A framework for clustering evolving data streams
-
C. C. Aggarwal, J. Han, J. Wang, and P. S. Yu. A framework for clustering evolving data streams. In VLDB, pages 81-92, 2003.
-
(2003)
VLDB
, pp. 81-92
-
-
Aggarwal, C.C.1
Han, J.2
Wang, J.3
Yu, P.S.4
-
3
-
-
85011117106
-
Anytime measures for top-k algorithms
-
B. Arai, G. Das, D. Gunopulos, and N. Koudas. Anytime measures for top-k algorithms. In VLDB, pages 914-925, 2007.
-
(2007)
VLDB
, pp. 914-925
-
-
Arai, B.1
Das, G.2
Gunopulos, D.3
Koudas, N.4
-
4
-
-
0009907231
-
Actuarial mathematics
-
Itasca, IL
-
N. L. Bowers, H. U. Gerber, J. C. Hickman, D. A. Jones, and C. J. Nesbitt. Actuarial Mathematics. Society of Actuaries, Itasca, IL, 1997.
-
(1997)
Society of Actuaries
-
-
Bowers, N.L.1
Gerber, H.U.2
Hickman, J.C.3
Jones, D.A.4
Nesbitt, C.J.5
-
5
-
-
33745434639
-
Density-based clustering over an evolving data stream with noise
-
F. Cao, M. Ester, W. Qian, and A. Zhou. Density-based clustering over an evolving data stream with noise. In SDM, 2006.
-
(2006)
SDM
-
-
Cao, F.1
Ester, M.2
Qian, W.3
Zhou, A.4
-
7
-
-
1942450879
-
Anytime interval-valued outputs for kernel machines: Fast support vector machine classiffication via distance geometry
-
D. DeCoste. Anytime interval-valued outputs for kernel machines: Fast support vector machine classiffication via distance geometry. In ICML, 2002.
-
(2002)
ICML
-
-
Decoste, D.1
-
8
-
-
70450099261
-
EDISKCO: Energy efficient distributed in-sensor-network k-center clustering with outliers
-
M. Hassani, E. Müller, and T. Seidl. EDISKCO: energy efficient distributed in-sensor-network k-center clustering with outliers. In Proc. SensorKDD 2009, pages 39-48, 2009.
-
(2009)
Proc. SensorKDD 2009
, pp. 39-48
-
-
Hassani, M.1
Müller, E.2
Seidl, T.3
-
10
-
-
34547616634
-
Adaptive non-linear clustering in data streams
-
DOI 10.1145/1183614.1183636, Proceedings of the 15th ACM Conference on Information and Knowledge Management, CIKM 2006
-
A. Jain, Z. Zhang, and E. Y. Chang. Adaptive non-linear clustering in data streams. In CIKM, pages 122-131, 2006. (Pubitemid 47203559)
-
(2006)
International Conference on Information and Knowledge Management, Proceedings
, pp. 122-131
-
-
Jain, A.1
Zhang, Z.2
Chang, E.Y.3
-
11
-
-
77951189927
-
Self-adaptive anytime stream clustering
-
P. Kranen, I. Assent, C. Baldauf, and T. Seidl. Self-adaptive anytime stream clustering. In IEEE ICDM, pages 249-258, 2009.
-
(2009)
IEEE ICDM
, pp. 249-258
-
-
Kranen, P.1
Assent, I.2
Baldauf, C.3
Seidl, T.4
-
13
-
-
80051694149
-
MC-tree: Improving bayesian anytime classiffication
-
P. Kranen, S. Günnemann, S. Fries, and T. Seidl. MC-tree: Improving bayesian anytime classiffication. In 22nd SSDBM, Springer LNCS, 2010.
-
(2010)
22nd SSDBM, Springer LNCS
-
-
Kranen, P.1
Günnemann, S.2
Fries, S.3
Seidl, T.4
-
14
-
-
80051694851
-
Hierarchical clustering for real-time stream data with noise
-
to appear
-
P. Kranen, F. Reidl, F. S. Villaamil, and T. Seidl. Hierarchical clustering for real-time stream data with noise. In SSDBM (to appear), 2011.
-
(2011)
SSDBM
-
-
Kranen, P.1
Reidl, F.2
Villaamil, F.S.3
Seidl, T.4
-
15
-
-
68749087011
-
Harnessing the strengths of anytime algorithms for constant data streams
-
ECML PKDD Special Issue
-
P. Kranen and T. Seidl. Harnessing the strengths of anytime algorithms for constant data streams. DMKD Journal (19)2, ECML PKDD Special Issue, 2009.
-
(2009)
DMKD Journal
, vol.2
, Issue.19
-
-
Kranen, P.1
Seidl, T.2
-
16
-
-
62449204280
-
A grid and fractal dimension-based data stream clustering algorithm
-
G. Lin and L. Chen. A grid and fractal dimension-based data stream clustering algorithm. In ISISE, volume 1, pages 66 -70, 2008.
-
(2008)
ISISE
, vol.1
, pp. 66-70
-
-
Lin, G.1
Chen, L.2
-
17
-
-
68749121246
-
Indexing density models for incremental learning and anytime classiffication on data streams
-
T. Seidl, I. Assent, P. Kranen, R. Krieger, and J. Herrmann. Indexing density models for incremental learning and anytime classiffication on data streams. In EDBT/ICDT, 2009.
-
(2009)
EDBT/ICDT
-
-
Seidl, T.1
Assent, I.2
Kranen, P.3
Krieger, R.4
Herrmann, J.5
-
18
-
-
79951755130
-
Polishing the right apple: Anytime classiffication also benefits data streams with constant arrival times
-
J. Shieh and E. Keogh. Polishing the right apple: Anytime classiffication also benefits data streams with constant arrival times. In Proc. of ICDM, 2010.
-
(2010)
Proc. of ICDM
-
-
Shieh, J.1
Keogh, E.2
-
19
-
-
69049112118
-
Combining multiple interrelated streams for incremental clustering
-
Z. F. Siddiqui and M. Spiliopoulou. Combining multiple interrelated streams for incremental clustering. In SSDBM, pages 535-552, 2009.
-
(2009)
SSDBM
, pp. 535-552
-
-
Siddiqui, Z.F.1
Spiliopoulou, M.2
-
20
-
-
0035788947
-
A streaming ensemble algorithm (sea) for large-scale classiffication
-
W. N. Street and Y. Kim. A streaming ensemble algorithm (sea) for large-scale classiffication. In Proc. of the 7th ACM KDD, pages 377-382, 2001.
-
(2001)
Proc. of the 7th ACM KDD
, pp. 377-382
-
-
Street, W.N.1
Kim, Y.2
-
21
-
-
72849144638
-
Anytime classiffication using the nearest neighbor algorithm with applications to stream mining
-
K. Ueno, X. Xi, E. J. Keogh, and D.-J. Lee. Anytime classiffication using the nearest neighbor algorithm with applications to stream mining. In ICDM, 2006.
-
(2006)
ICDM
-
-
Ueno, K.1
Xi, X.2
Keogh, E.J.3
Lee, D.-J.4
-
22
-
-
77952415079
-
Mining concept-drifting data streams using ensemble classifiers
-
H. Wang, W. Fan, P. S. Yu, and J. Han. Mining concept-drifting data streams using ensemble classifiers. In Proc. of the 9th ACM KDD, pages 226-235, 2003.
-
(2003)
Proc. of the 9th ACM KDD
, pp. 226-235
-
-
Wang, H.1
Fan, W.2
Yu, P.S.3
Han, J.4
-
23
-
-
35148836033
-
Classifying under computational resource constraints: Anytime classiffication using probabilistic estimators
-
Y. Yang, G. I. Webb, K. B. Korb, and K. M. Ting. Classifying under computational resource constraints: anytime classiffication using probabilistic estimators. Machine Learning, 69(1), 2007.
-
(2007)
Machine Learning
, vol.69
, Issue.1
-
-
Yang, Y.1
Webb, G.I.2
Korb, K.B.3
Ting, K.M.4
-
24
-
-
80051679865
-
Autocannibalistic and anyspace indexing algorithms with application to sensor data mining
-
L. Ye, X. Wang, E. J. Keogh, and A. Mafra-Neto. Autocannibalistic and anyspace indexing algorithms with application to sensor data mining. In SDM, pages 85-96, 2009.
-
(2009)
SDM
, pp. 85-96
-
-
Ye, L.1
Wang, X.2
Keogh, E.J.3
Mafra-Neto, A.4
-
25
-
-
0030157145
-
BIRCH: An efficient data clustering method for very large databases
-
T. Zhang, R. Ramakrishnan, and M. Livny. BIRCH: an efficient data clustering method for very large databases. In SIGMOD, 1996.
-
(1996)
SIGMOD
-
-
Zhang, T.1
Ramakrishnan, R.2
Livny, M.3
|