-
1
-
-
0039253822
-
Finding generalized projected clusters in high dimensional spaces
-
C. C. Aggarwal and P. S. Yu, Finding generalized projected clusters in high dimensional spaces. In SIGMOD Conference, pages 70-81, 2000.
-
(2000)
SIGMOD Conference
, pp. 70-81
-
-
Aggarwal, C.C.1
Yu, P.S.2
-
2
-
-
0032090765
-
Automatic subspace clustering of high dimensional data for data mining applications
-
R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan. Automatic subspace clustering of high dimensional data for data mining applications. In SIGMOD Conference, pages 94-105, 1998.
-
(1998)
SIGMOD Conference
, pp. 94-105
-
-
Agrawal, R.1
Gehrke, J.2
Gunopulos, D.3
Raghavan, P.4
-
3
-
-
0347172110
-
OPTICS: Ordering points to identify the clustering structure
-
M. Ankerst, M. M. Breunig, H.-P. Kriegel, and J. Sander. OPTICS: Ordering points to identify the clustering structure. In SIGMOD Conference, pages 49-60, 1999.
-
(1999)
SIGMOD Conference
, pp. 49-60
-
-
Ankerst, M.1
Breunig, M.M.2
Kriegel, H.-P.3
Sander, J.4
-
5
-
-
33749545220
-
Robust information-theoretic clustering
-
C. Böhm, C. Faloutsos, J.-Y. Pan, and C. Plant. Robust information-theoretic clustering. In KDD Conference, pages 65-75, 2006.
-
(2006)
KDD Conference
, pp. 65-75
-
-
Böhm, C.1
Faloutsos, C.2
Pan, J.-Y.3
Plant, C.4
-
6
-
-
14544300820
-
Computing clusters of correlation connected objects
-
C. Böhm, K. Kailing, P. Kröger, and A. Zimek. Computing clusters of correlation connected objects. In SIGMOD Conference, pages 455-466, 2004.
-
(2004)
SIGMOD Conference
, pp. 455-466
-
-
Böhm, C.1
Kailing, K.2
Kröger, P.3
Zimek, A.4
-
7
-
-
0039253819
-
Lof: Identifying density-based local outliers
-
M. M. Breunig, H.-P. Kriegel, R. T. Ng, and J. Sander. Lof: Identifying density-based local outliers. In SIGMOD Conference, pages 93-104, 2000.
-
(2000)
SIGMOD Conference
, pp. 93-104
-
-
Breunig, M.M.1
Kriegel, H.-P.2
Ng, R.T.3
Sander, J.4
-
8
-
-
0002629270
-
Maximum Likelihood from Incomplete Data via the EM Algorithm
-
A. P. Dempster, N. M. Laird, and D. B. Rubin. "Maximum Likelihood from Incomplete Data via the EM Algorithm". In J Roy Stat Soc, number 39, pages 1-31, 1977.
-
(1977)
J Roy Stat Soc
, Issue.39
, pp. 1-31
-
-
Dempster, A.P.1
Laird, N.M.2
Rubin, D.B.3
-
9
-
-
14344257496
-
K-means clustering via principal component analysis
-
C. H. Q. Ding and X. He. K-means clustering via principal component analysis. In ICML Conference, 2004.
-
(2004)
ICML Conference
-
-
Ding, C.H.Q.1
He, X.2
-
10
-
-
0000550189
-
A density-based algorithm for discovering clusters in large spatial databases with noise
-
M. Ester, H.-P. Kriegel, J. Sander, and X. Xu. A density-based algorithm for discovering clusters in large spatial databases with noise. In KDD Conference, 1996.
-
(1996)
KDD Conference
-
-
Ester, M.1
Kriegel, H.-P.2
Sander, J.3
Xu, X.4
-
14
-
-
13444262051
-
A software tool for the exponential power distribution: The normalp package
-
A. Mineo and M. Ruggieri. A software tool for the exponential power distribution: The normalp package. Journal of Statistical Software, 12(4), 1 2005.
-
(2005)
Journal of Statistical Software
, vol.12
, Issue.4
, pp. 1
-
-
Mineo, A.1
Ruggieri, M.2
-
16
-
-
0001820920
-
X-means: Extending K-means with efficient estimation of the number of clusters
-
D. Pelleg and A. Moore. X-means: Extending K-means with efficient estimation of the number of clusters. In ICML Conference, pages 727-734, 2000.
-
(2000)
ICML Conference
, pp. 727-734
-
-
Pelleg, D.1
Moore, A.2
-
17
-
-
29844449492
-
CURLER: Finding and visualizing nonlinear correlation clusters
-
A. K. Tung, X. Xu, and B. C. Ooi. CURLER: Finding and visualizing nonlinear correlation clusters. In SIGMOD Conference, pages 467-478, 2005.
-
(2005)
SIGMOD Conference
, pp. 467-478
-
-
Tung, A.K.1
Xu, X.2
Ooi, B.C.3
-
18
-
-
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 Conference, pages 103-114, 1996.
-
(1996)
SIGMOD Conference
, pp. 103-114
-
-
Zhang, T.1
Ramakrishnan, R.2
Livny, M.3
|