-
1
-
-
0347718066
-
Fast algorithms for projected clustering
-
C. C. Aggarwal, C. M. Procopiuc, J. L. Wolf, P. S. Yu, and J. S. Park. Fast algorithms for projected clustering. In Proc. SIGMOD, 1999.
-
(1999)
Proc. SIGMOD
-
-
Aggarwal, C.C.1
Procopiuc, C.M.2
Wolf, J.L.3
Yu, P.S.4
Park, J.S.5
-
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 Proc. SIGMOD, 1998.
-
(1998)
Proc. SIGMOD
-
-
Agrawal, R.1
Gehrke, J.2
Gunopulos, D.3
Raghavan, P.4
-
3
-
-
9444260615
-
Fast algorithms for mining association rules
-
R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In Proc. SIGMOD, 1994.
-
(1994)
Proc. SIGMOD
-
-
Agrawal, R.1
Srikant, R.2
-
5
-
-
84891128351
-
Less is more: Nonredundant subspace clustering
-
I. Assent, E. Müller, S. Günnemann, R. Krieger, and T. Seidl. Less is more: Nonredundant subspace clustering. In Proc. ACM SIGKDD Workshop MultiClust, 2010.
-
(2010)
Proc ACM SIGKDD Workshop MultiClust
-
-
Assent, I.1
Müller, E.2
Günnemann, S.3
Krieger, R.4
Seidl, T.5
-
6
-
-
84873117260
-
COALA: A novel approach for the extraction of an alternate clustering of high quality and high dissimilarity
-
E. Bae and J. Bailey. COALA: a novel approach for the extraction of an alternate clustering of high quality and high dissimilarity. In Proc. ICDM, 2006.
-
(2006)
Proc. ICDM
-
-
Bae, E.1
Bailey, J.2
-
7
-
-
0032091573
-
Efficiently mining long patterns from databases
-
R. Bayardo. Efficiently mining long patterns from databases. In Proc. SIGMOD, pages 85-93, 1998.
-
(1998)
Proc. SIGMOD
, pp. 85-93
-
-
Bayardo, R.1
-
8
-
-
0012908433
-
Density-based indexing for approximate nearest-neighbor queries
-
K. P. Bennett, U. Fayyad, and D. Geiger. Density-based indexing for approximate nearest-neighbor queries. In Proc. KDD, 1999.
-
(1999)
Proc. KDD
-
-
Bennett, K.P.1
Fayyad, U.2
Geiger, D.3
-
9
-
-
84891137021
-
Quality of similarity rankings in time series
-
T. Bernecker, M. E. Houle, H.-P. Kriegel, P. Kröger, M. Renz, E. Schubert, and A. Zimek. Quality of similarity rankings in time series. In Proc. SSTD, 2011.
-
(2011)
Proc. SSTD
-
-
Bernecker, T.1
Houle, M.E.2
Kriegel, H.-P.3
Kröger, P.4
Renz, M.5
Schubert, E.6
Zimek, A.7
-
12
-
-
85016670270
-
ITCH: Information-theoretic cluster hierarchies
-
C. Böhm, F. Fiedler, A. Oswald, C. Plant, B. Wackersreuther, and P. Wackersreuther. ITCH: information-theoretic cluster hierarchies. In Proc. ECML PKDD, 2010.
-
(2010)
Proc. ECML PKDD
-
-
Böhm, C.1
Fiedler, F.2
Oswald, A.3
Plant, C.4
Wackersreuther, B.5
Wackersreuther, P.6
-
14
-
-
0002646822
-
Entropy-based subspace clustering for mining numerical data
-
C. H. Cheng, A. W.-C. Fu, and Y. Zhang. Entropy-based subspace clustering for mining numerical data. In Proc. KDD, pages 84-93, 1999.
-
(1999)
Proc. KDD
, pp. 84-93
-
-
Cheng, C.H.1
Fu, A.W.-C.2
Zhang, Y.3
-
15
-
-
84873124076
-
Generation of alternative clusterings using the CAMI approach
-
X. H. Dang and J. Bailey. Generation of alternative clusterings using the CAMI approach. In Proc. SDM, 2010.
-
(2010)
Proc. SDM
-
-
Dang, X.H.1
Bailey, J.2
-
16
-
-
84873178417
-
A SAT-based framework for efficient constrained clustering
-
I. Davidson, S. S. Ravi, and L. Shamis. A SAT-based framework for efficient constrained clustering. In Proc. SDM, 2010.
-
(2010)
Proc. SDM
-
-
Davidson, I.1
Ravi, S.S.2
Shamis, L.3
-
17
-
-
80855129853
-
Maximum entropy models and subjective interestingness: An application to tiles in binary databases
-
T. De Bie. Maximum entropy models and subjective interestingness: an application to tiles in binary databases. Data Min. Knowl. Disc., 2010.
-
(2010)
Data Min. Knowl. Disc.
-
-
De Bie, T.1
-
18
-
-
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 Proc. KDD, 1996.
-
(1996)
Proc. KDD
-
-
Ester, M.1
Kriegel, H.-P.2
Sander, J.3
Xu, X.4
-
19
-
-
84891109991
-
On using class-labels in evaluation of clusterings
-
I. Färber, S. Günnemann, H.-P. Kriegel, P. Kröger, E. Müller, E. Schubert, T. Seidl, and A. Zimek. On using class-labels in evaluation of clusterings. In Proc. ACM SIGKDD Workshop MultiClust, 2010.
-
(2010)
Proc ACM SIGKDD Workshop MultiClust
-
-
Färber, I.1
Günnemann, S.2
Kriegel, H.-P.3
Kröger, P.4
Müller, E.5
Schubert, E.6
Seidl, T.7
Zimek, A.8
-
20
-
-
34249788454
-
The concentration of fractional distances
-
D. Francois, V.Wertz, and M. Verleysen. The concentration of fractional distances. IEEE TKDE, 19(7):873-886, 2007.
-
(2007)
IEEE TKDE
, vol.19
, Issue.7
, pp. 873-886
-
-
Francois, D.1
Wertz, V.2
Verleysen, M.3
-
22
-
-
37049039428
-
Assessing data mining results via swap randomization
-
A. Gionis, H. Mannila, T. Mielikäinen, and P. Tsaparas. Assessing data mining results via swap randomization. ACM TKDD, 1(3), 2007.
-
(2007)
ACM TKDD
, vol.1
, Issue.3
-
-
Gionis, A.1
Mannila, H.2
Mielikäinen, T.3
Tsaparas, P.4
-
27
-
-
74549217295
-
Detection of orthogonal concepts in subspaces of high dimensional data
-
S. Günnemann, E. Müller, I. Färber, and T. Seidl. Detection of orthogonal concepts in subspaces of high dimensional data. In Proc. CIKM, 2009.
-
(2009)
Proc. CIKM
-
-
Günnemann, S.1
Müller, E.2
Färber, I.3
Seidl, T.4
-
28
-
-
70350663120
-
Tell me something i don't know: Randomization strategies for iterative data mining
-
S. Hanhijärvi, M. Ojala, N. Vuokko, K. Puolamäki, N. Tatti, and H. Mannila. Tell me something I don't know: randomization strategies for iterative data mining. In Proc. KDD, pages 379-388, 2009.
-
(2009)
Proc. KDD
, pp. 379-388
-
-
Hanhijärvi, S.1
Ojala, M.2
Vuokko, N.3
Puolamäki, K.4
Tatti, N.5
Mannila, H.6
-
29
-
-
80755126849
-
Can sharedneighbor distances defeat the curse of dimensionality?
-
M. E. Houle, H.-P. Kriegel, P. Kröger, E. Schubert, and A. Zimek. Can sharedneighbor distances defeat the curse of dimensionality? In Proc. SSDBM, 2010.
-
(2010)
Proc SSDBM
-
-
Houle, M.E.1
Kriegel, H.-P.2
Kröger, P.3
Schubert, E.4
Zimek, A.5
-
30
-
-
70049108502
-
Simultaneous unsupervised learning of disparate clusterings
-
P. Jain, R. Meka, and I. S. Dhillon. Simultaneous unsupervised learning of disparate clusterings. Stat. Anal. Data Min., 1(3):195-210, 2008.
-
(2008)
Stat. Anal. Data Min.
, vol.1
, Issue.3
, pp. 195-210
-
-
Jain, P.1
Meka, R.2
Dhillon, I.S.3
-
31
-
-
33750297146
-
Density-connected subspace clustering for high-dimensional data
-
K. Kailing, H.-P. Kriegel, and P. Kröger. Density-connected subspace clustering for high-dimensional data. In Proc. SDM, 2004.
-
(2004)
Proc. SDM
-
-
Kailing, K.1
Kriegel, H.-P.2
Kröger, P.3
-
33
-
-
77956254622
-
An information-theoretic approach to finding informative noisy tiles in binary databases
-
K.-N. Kontonasios and T. DeBie. An information-theoretic approach to finding informative noisy tiles in binary databases. In Proc. SDM, 2010.
-
(2010)
Proc. SDM
-
-
Kontonasios, K.-N.1
Debie, T.2
-
34
-
-
67149084291
-
Clustering high dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering
-
H.-P. Kriegel, P. Kröger, and A. Zimek. Clustering high dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering. ACM TKDD, 3(1):1-58, 2009.
-
(2009)
ACM TKDD
, vol.3
, Issue.1
, pp. 1-58
-
-
Kriegel, H.-P.1
Kröger, P.2
Zimek, A.3
-
36
-
-
84891112684
-
Subspace clustering, ensemble clustering, alternative clustering, multiview clustering: What can we learn from each other?
-
H.-P. Kriegel and A. Zimek. Subspace clustering, ensemble clustering, alternative clustering, multiview clustering: What can we learn from each other? In Proc. ACM SIGKDD Workshop MultiClust, 2010.
-
(2010)
Proc ACM SIGKDD Workshop MultiClust
-
-
Kriegel, H.-P.1
Zimek, A.2
-
37
-
-
34548723854
-
Distance based subspace clustering with flexible dimension partitioning
-
G. Liu, J. Li, K. Sim, and L. Wong. Distance based subspace clustering with flexible dimension partitioning. In Proc. ICDE, 2007.
-
(2007)
Proc. ICDE
-
-
Liu, G.1
Li, J.2
Sim, K.3
Wong, L.4
-
38
-
-
0002056465
-
Version spaces: A candidate elimination approach to rule learning
-
T. M. Mitchell. Version spaces: A candidate elimination approach to rule learning. In Proc. IJCAI, 1977.
-
(1977)
Proc. IJCAI
-
-
Mitchell, T.M.1
-
39
-
-
84857180195
-
Efficient mining of all margin-closed itemsets with applications in temporal knowledge discovery and classification by compression
-
F. Moerchen, M. Thies, and A. Ultsch. Efficient mining of all margin-closed itemsets with applications in temporal knowledge discovery and classification by compression. KAIS, 2010.
-
(2010)
KAIS
-
-
Moerchen, F.1
Thies, M.2
Ultsch, A.3
-
40
-
-
65449163900
-
Finding non-redundant, statistically significant regions in high dimensional data: A novel approach to projected and subspace clustering
-
G. Moise and J. Sander. Finding non-redundant, statistically significant regions in high dimensional data: a novel approach to projected and subspace clustering. In Proc. KDD, 2008.
-
(2008)
Proc. KDD
-
-
Moise, G.1
Sander, J.2
-
41
-
-
77951149821
-
Relevant subspace clustering: Mining the most interesting non-redundant concepts in high dimensional data
-
E. Müller, I. Assent, S. Günnemann, R. Krieger, and T. Seidl. Relevant subspace clustering: Mining the most interesting non-redundant concepts in high dimensional data. In Proc. ICDM, 2009.
-
(2009)
Proc. ICDM
-
-
Müller, E.1
Assent, I.2
Günnemann, S.3
Krieger, R.4
Seidl, T.5
-
42
-
-
77951200841
-
DensEst: Density estimation for data mining in high dimensional spaces
-
E. Müller, I. Assent, R. Krieger, S. Günnemann, and T. Seidl. DensEst: density estimation for data mining in high dimensional spaces. In Proc. SDM, 2009.
-
(2009)
Proc. SDM
-
-
Müller, E.1
Assent, I.2
Krieger, R.3
Günnemann, S.4
Seidl, T.5
-
44
-
-
79951736794
-
Assessing data mining results on matrices with randomization
-
M. Ojala. Assessing data mining results on matrices with randomization. In Proc. ICDM, 2010.
-
(2010)
Proc. ICDM
-
-
Ojala, M.1
-
45
-
-
77950233355
-
Randomization methods for assessing data analysis results on real-valued matrices
-
M. Ojala, N. Vuokko, A. Kallio, N. Haiminen, and H. Mannila. Randomization methods for assessing data analysis results on real-valued matrices. Stat. Anal. Data Min., 2(4):209-230, 2009.
-
(2009)
Stat. Anal. Data Min.
, vol.2
, Issue.4
, pp. 209-230
-
-
Ojala, M.1
Vuokko, N.2
Kallio, A.3
Haiminen, N.4
Mannila, H.5
-
47
-
-
70350663111
-
A principled and flexible framework for finding alternative clusterings
-
Z. J. Qi and I. Davidson. A principled and flexible framework for finding alternative clusterings. In Proc. KDD, 2009.
-
(2009)
Proc. KDD
-
-
Qi, Z.J.1
Davidson, I.2
-
48
-
-
0035237805
-
Rich probabilistic models for gene expression
-
E. Segal, B. Taskar, A. Gasch, N. Friedman, and D. Koller. Rich probabilistic models for gene expression. Bioinformatics, 17(Suppl. 1):S243-S252, 2001.
-
(2001)
Bioinformatics
, vol.17
, Issue.SUPPL. 1
-
-
Segal, E.1
Taskar, B.2
Gasch, A.3
Friedman, N.4
Koller, D.5
-
49
-
-
79960089996
-
Krimp: Mining itemsets that compress
-
J. Vreeken, M. van Leeuwen, and A. Siebes. Krimp: Mining itemsets that compress. Data Min. Knowl. Disc., 23(1):169-214, 2010.
-
(2010)
Data Min. Knowl. Disc.
, vol.23
, Issue.1
, pp. 169-214
-
-
Vreeken, J.1
Van Leeuwen, M.2
Siebes, A.3
-
50
-
-
34249653461
-
Discovering significant patterns
-
G. I. Webb. Discovering significant patterns. Mach. Learn., 68(1):1-33, 2007.
-
(2007)
Mach. Learn.
, vol.68
, Issue.1
, pp. 1-33
-
-
Webb, G.I.1
-
51
-
-
32344439809
-
Summarizing itemset patterns: A profilebased approach
-
X. Yan, H. Cheng, J. Han, and D. Xin. Summarizing itemset patterns: a profilebased approach. In Proc. KDD, 2005.
-
(2005)
Proc. KDD
-
-
Yan, X.1
Cheng, H.2
Han, J.3
Xin, D.4
|