-
1
-
-
17044376078
-
Subspace clustering for high dimensional data: a review
-
Parsons L, Haque E, Liu H. Subspace clustering for high dimensional data: a review. SIGKDD Explor 2004, 6:90-105.
-
(2004)
SIGKDD Explor
, vol.6
, pp. 90-105
-
-
Parsons, L.1
Haque, E.2
Liu, H.3
-
2
-
-
84925655451
-
Subspace clustering techniques
-
Liu L, Özsu MT, eds New York, NY: Springer
-
Kröger P, Zimek A. Subspace clustering techniques. In: Liu L, Özsu MT, eds. Encyclopedia of Database Systems. New York, NY: Springer; 2009 2873-2875.
-
(2009)
Encyclopedia of Database Systems
, pp. 2873-2875
-
-
Kröger, P.1
Zimek, A.2
-
3
-
-
67149084291
-
Clustering high dimensional data: a survey on subspace clustering, pattern-based clustering, and correlation clustering
-
Kriegel H-P, Kröger P, Zimek A. Clustering high dimensional data: a survey on subspace clustering, pattern-based clustering, and correlation clustering. ACM Trans Knowl Discov Data 2009, 3:1-58.
-
(2009)
ACM Trans Knowl Discov Data
, vol.3
, pp. 1-58
-
-
Kriegel, H.-P.1
Kröger, P.2
Zimek, A.3
-
6
-
-
41949141213
-
-
Philadelphia, PA: Society for Industrial and Applied Mathematics
-
Gan G, Ma C, Wu J. Data Clustering. Theory, Algorithms, and Applications. Philadelphia, PA: Society for Industrial and Applied Mathematics; 2007.
-
(2007)
Data Clustering. Theory, Algorithms, and Applications.
-
-
Gan, G.1
Ma, C.2
Wu, J.3
-
7
-
-
71949123741
-
Subspace and projected clustering: experimental evaluation and analysis
-
Moise G, Zimek A, Kröger P, Kriegel H-P, Sander J. Subspace and projected clustering: experimental evaluation and analysis. Knowl Inf Syst 2009, 21:299-326.
-
(2009)
Knowl Inf Syst
, vol.21
, pp. 299-326
-
-
Moise, G.1
Zimek, A.2
Kröger, P.3
Kriegel, H.-P.4
Sander, J.5
-
8
-
-
84865086248
-
Evaluating clustering in subspace projections of high dimensional data
-
Lyon, France
-
Müller E, Günnemann S, Assent I, Seidl T. Evaluating clustering in subspace projections of high dimensional data. In: Proceedings of the 35th International Conference on Very Large Data Bases (VLDB), Lyon, France; 2009.
-
(2009)
Proceedings of the 35th International Conference on Very Large Data Bases (VLDB)
-
-
Müller, E.1
Günnemann, S.2
Assent, I.3
Seidl, T.4
-
9
-
-
0038670812
-
Searching in highdimensional spaces: index structures for improving the performance of multimedia databases
-
Böhm C, Berchtold S, Keim DA. Searching in highdimensional spaces: index structures for improving the performance of multimedia databases. ACM Comput Surv 2001, 33:322-373.
-
(2001)
ACM Comput Surv
, vol.33
, pp. 322-373
-
-
Böhm, C.1
Berchtold, S.2
Keim, D.A.3
-
11
-
-
0002086686
-
When is 'nearest neighbor' meaningful?
-
Jerusalem, Israel
-
Beyer K, Goldstein J, Ramakrishnan R, Shaft U. When is 'nearest neighbor' meaningful? In: Proceedings of the 7th International Conference on Database Theory (ICDT), Jerusalem, Israel; 1999.
-
(1999)
Proceedings of the 7th International Conference on Database Theory (ICDT)
-
-
Beyer, K.1
Goldstein, J.2
Ramakrishnan, R.3
Shaft, U.4
-
16
-
-
77955045250
-
Can shared-neighbor distances defeat the curse of dimensionality?
-
Heidelberg, Germany
-
Houle ME, Kriegel H-P, Kröger P, Schubert E, Zimek A. Can shared-neighbor distances defeat the curse of dimensionality? In: Proceedings of the 22nd International Conference on Scientific and Statistical Database Management (SSDBM), Heidelberg, Germany; 2010.
-
(2010)
Proceedings of the 22nd International Conference on Scientific and Statistical Database Management (SSDBM)
-
-
Houle, M.E.1
Kriegel, H.-P.2
Kröger, P.3
Schubert, E.4
Zimek, A.5
-
17
-
-
84873197289
-
Quality of similarity rankings in time series
-
Minneapolis, MN
-
Bernecker T, Houle ME, Kriegel H-P, Kröger P, Renz M, Schubert E, Zimek A. Quality of similarity rankings in time series. In: Proceedings of the 12th International Symposium on Spatial and Temporal Databases (SSTD), Minneapolis, MN; 2011.
-
(2011)
Proceedings of the 12th International Symposium on Spatial and Temporal Databases (SSTD)
-
-
Bernecker, T.1
Houle, M.E.2
Kriegel, H.-P.3
Kröger, P.4
Renz, M.5
Schubert, E.6
Zimek, A.7
-
18
-
-
33749545528
-
Deriving quantitative models for correlation clusters
-
Philadelphia, PA
-
Achtert E, Böhm C, Kriegel H-P, Kröger P, Zimek A. Deriving quantitative models for correlation clusters. In: Proceedings of the 12th ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD), Philadelphia, PA; 2006.
-
(2006)
Proceedings of the 12th ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD)
-
-
Achtert, E.1
Böhm, C.2
Kriegel, H.-P.3
Kröger, P.4
Zimek, A.5
-
19
-
-
0035049111
-
On the 'dimensionality curse' and the 'self-similarity blessing'
-
Korn F, Pagel B-U, Faloutsos C. On the 'dimensionality curse' and the 'self-similarity blessing'. IEEE Trans Knowl Data Eng 2001, 13:96-111.
-
(2001)
IEEE Trans Knowl Data Eng
, vol.13
, pp. 96-111
-
-
Korn, F.1
Pagel, B.-U.2
Faloutsos, C.3
-
20
-
-
84945923591
-
A method for comparing two hierarchical clusterings
-
Fowlkes EB, Mallows CL. A method for comparing two hierarchical clusterings. J Am Stat Assoc 1983, 78:553-569.
-
(1983)
J Am Stat Assoc
, vol.78
, pp. 553-569
-
-
Fowlkes, E.B.1
Mallows, C.L.2
-
21
-
-
33847457966
-
An examination of the effect of six types of error perturbation on fifteen clustering algorithms
-
Milligan GW. An examination of the effect of six types of error perturbation on fifteen clustering algorithms. Psychometrika 1980, 45:325-342.
-
(1980)
Psychometrika
, vol.45
, pp. 325-342
-
-
Milligan, G.W.1
-
23
-
-
21844501258
-
Weighting and selection of variables for cluster analysis
-
Gnanadesikan R, Kettenring JR, Tsao SL. Weighting and selection of variables for cluster analysis. J Classif 1995, 12:113-136.
-
(1995)
J Classif
, vol.12
, pp. 113-136
-
-
Gnanadesikan, R.1
Kettenring, J.R.2
Tsao, S.L.3
-
24
-
-
41449108683
-
Selection of variables in cluster analysis: an empirical comparison of eight procedures
-
Steinley D, Brusco MJ. Selection of variables in cluster analysis: an empirical comparison of eight procedures. Psychometrika 2008, 73:125-144.
-
(2008)
Psychometrika
, vol.73
, pp. 125-144
-
-
Steinley, D.1
Brusco, M.J.2
-
25
-
-
33745561205
-
An introduction to variable and feature selection
-
Guyon I, Elisseeff A. An introduction to variable and feature selection. J Mach Learn Res 2003, 3:1157-1182, 2003.
-
(2003)
J Mach Learn Res 2003
, vol.3
, pp. 1157-1182
-
-
Guyon, I.1
Elisseeff, A.2
-
26
-
-
84866454507
-
Distance-preserving dimensionality reduction
-
Yang L. Distance-preserving dimensionality reduction. WIREs Data Min Knowl Discov 2011, 1:369-380.
-
(2011)
WIREs Data Min Knowl Discov
, vol.1
, pp. 369-380
-
-
Yang, L.1
-
27
-
-
84880195636
-
Spatial outlier detection: data, algorithms, visualizations
-
Minneapolis, MN
-
Achtert E, Hettab A, Kriegel H-P, Schubert E, Zimek A. Spatial outlier detection: data, algorithms, visualizations. In: Proceedings of the 12th International Symposium on Spatial and Temporal Databases (SSTD), Minneapolis, MN; 2011.
-
(2011)
Proceedings of the 12th International Symposium on Spatial and Temporal Databases (SSTD)
-
-
Achtert, E.1
Hettab, A.2
Kriegel, H.-P.3
Schubert, E.4
Zimek, A.5
-
30
-
-
0347718066
-
Fast algorithms for projected clustering
-
Philadelphia, PA
-
Aggarwal CC, Procopiuc CM, Wolf JL, Yu PS, Park JS. Fast algorithms for projected clustering. In: Proceedings of the ACM International Conference on Management of Data (SIGMOD), Philadelphia, PA; 1999.
-
(1999)
Proceedings of the ACM International Conference on Management of Data (SIGMOD)
-
-
Aggarwal, C.C.1
Procopiuc, C.M.2
Wolf, J.L.3
Yu, P.S.4
Park, J.S.5
-
31
-
-
0032090765
-
Automatic subspace clustering of high dimensional data for data mining applications
-
Seattle, WA
-
Agrawal R, Gehrke J, Gunopulos D, Raghavan P. Automatic subspace clustering of high dimensional data for data mining applications. In: Proceedings of the ACM International Conference on Management of Data (SIGMOD), Seattle, WA; 1998.
-
(1998)
Proceedings of the ACM International Conference on Management of Data (SIGMOD)
-
-
Agrawal, R.1
Gehrke, J.2
Gunopulos, D.3
Raghavan, P.4
-
33
-
-
34347228671
-
An entropy weighting k-means algorithm for subspace clustering of high-dimensional sparse data
-
Jing L, Ng MK, Huang JZ. An entropy weighting k-means algorithm for subspace clustering of high-dimensional sparse data. IEEE Trans Knowl Data Eng 2007, 19:1026-1041.
-
(2007)
IEEE Trans Knowl Data Eng
, vol.19
, pp. 1026-1041
-
-
Jing, L.1
Ng, M.K.2
Huang, J.Z.3
-
34
-
-
0742324835
-
FINDIT: a fast and intelligent subspace clustering algorithm using dimension voting
-
Woo K-G, Lee J-H, Kim M-H, Lee Y-J. FINDIT: a fast and intelligent subspace clustering algorithm using dimension voting. Inf Softw Technol 2004, 46:255-271.
-
(2004)
Inf Softw Technol
, vol.46
, pp. 255-271
-
-
Woo, K.-G.1
Lee, J.-H.2
Kim, M.-H.3
Lee, Y.-J.4
-
36
-
-
19544386608
-
Density connected clustering with local subspace preferences
-
Brighton, UK
-
Böhm C, Kailing K, Kriegel H-P, Kröger P. Density connected clustering with local subspace preferences. In: Proceedings of the 4th IEEE International Conference on Data Mining (ICDM), Brighton, UK; 2004.
-
(2004)
Proceedings of the 4th IEEE International Conference on Data Mining (ICDM)
-
-
Böhm, C.1
Kailing, K.2
Kriegel, H.-P.3
Kröger, P.4
-
37
-
-
2942560799
-
Subspace clustering of high dimensional data
-
Lake Buena Vista, FL
-
Domeniconi C, Papadopoulos D, Gunopulos D, Ma S. Subspace clustering of high dimensional data. In: Proceedings of the 4th SIAM International Conference on Data Mining (SDM), Lake Buena Vista, FL; 2004.
-
(2004)
Proceedings of the 4th SIAM International Conference on Data Mining (SDM)
-
-
Domeniconi, C.1
Papadopoulos, D.2
Gunopulos, D.3
Ma, S.4
-
38
-
-
8644255832
-
Clustering objects on subsets of attributes
-
Friedman JH, Meulman JJ. Clustering objects on subsets of attributes. J R Stat Soc B 2004, 66:825-849.
-
(2004)
J R Stat Soc B
, vol.66
, pp. 825-849
-
-
Friedman, J.H.1
Meulman, J.J.2
-
39
-
-
18144419389
-
Automated variable weighting in k-means type clustering
-
Huang JZ, Ng MK, Rong H, Li Z. Automated variable weighting in k-means type clustering. IEEE Trans Pattern Anal Mach Intell; 2005, 27:657-668.
-
(2005)
IEEE Trans Pattern Anal Mach Intell
, vol.27
, pp. 657-668
-
-
Huang, J.Z.1
Ng, M.K.2
Rong, H.3
Li, Z.4
-
41
-
-
33847338032
-
Locally adaptive metrics for clustering high dimensional data
-
Domeniconi C, Gunopulos D, Ma S, Yan B, M. Al Razgan, Papadopoulos D. Locally adaptive metrics for clustering high dimensional data. Data Min Knowl Discov 2007, 14:63-97.
-
(2007)
Data Min Knowl Discov
, vol.14
, pp. 63-97
-
-
Domeniconi, C.1
Gunopulos, D.2
Ma, S.3
Yan, B.4
Al Razgan, M.5
Papadopoulos, D.6
-
42
-
-
78650700300
-
Particle swarm optimizer for variable weighting in clustering high-dimensional data
-
Lu Y, Wang S, Li S, Zhou C. Particle swarm optimizer for variable weighting in clustering high-dimensional data. Mach Learn 2010, 82:43-70.
-
(2010)
Mach Learn
, vol.82
, pp. 43-70
-
-
Lu, Y.1
Wang, S.2
Li, S.3
Zhou, C.4
-
43
-
-
0002629270
-
Maximum likelihood from incomplete data via the EM algorithm
-
Dempster AP, Laird NM, Rubin DB. Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc B, 1977, 39:1-31.
-
(1977)
J R Stat Soc B
, vol.39
, pp. 1-31
-
-
Dempster, A.P.1
Laird, N.M.2
Rubin, D.B.3
-
44
-
-
0002646822
-
Entropy-based subspace clustering for mining numerical data
-
San Diego, CA
-
Cheng CH, Fu AW-C, Zhang Y. Entropy-based subspace clustering for mining numerical data. In: Proceedings of the 5th ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD), San Diego, CA; 1999, 84-93.
-
(1999)
Proceedings of the 5th ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD)
, pp. 84-93
-
-
Cheng, C.H.1
Fu, A.-C.2
Zhang, Y.3
-
46
-
-
47249137675
-
DUSC: dimensionality unbiased subspace clustering
-
Omaha, NE
-
Assent I, Krieger R, Müller E, Seidl T. DUSC: dimensionality unbiased subspace clustering. In: Proceedings of the 7th IEEE International Conference on Data Mining (ICDM), Omaha, NE; 2007.
-
(2007)
Proceedings of the 7th IEEE International Conference on Data Mining (ICDM)
-
-
Assent, I.1
Krieger, R.2
Müller, E.3
Seidl, T.4
-
47
-
-
77950250511
-
Efficient mining of distance-based subspace clusters
-
Liu G, Sim K, Li J, Wong L. Efficient mining of distance-based subspace clusters. Stat Anal Data Min 2009, 2:427-444.
-
(2009)
Stat Anal Data Min
, vol.2
, pp. 427-444
-
-
Liu, G.1
Sim, K.2
Li, J.3
Wong, L.4
-
49
-
-
0036361164
-
A Monte Carlo algorithm for fast projective clustering
-
Madison, WI
-
Procopiuc CM, Jones M, Agarwal PK, Murali TM. A Monte Carlo algorithm for fast projective clustering. In: Proceedings of the ACM International Conference on Management of Data (SIGMOD), Madison, WI; 2002.
-
(2002)
Proceedings of the ACM International Conference on Management of Data (SIGMOD)
-
-
Procopiuc, C.M.1
Jones, M.2
Agarwal, P.K.3
Murali, T.M.4
-
51
-
-
14644404956
-
Iterative projected clustering by subspace mining
-
Yiu ML, Mamoulis N. Iterative projected clustering by subspace mining. IEEE Trans Knowl Data Eng 2005, 17:176-189.
-
(2005)
IEEE Trans Knowl Data Eng
, vol.17
, pp. 176-189
-
-
Yiu, M.L.1
Mamoulis, N.2
-
52
-
-
34547251368
-
A generic framework for efficient subspace clustering of highdimensional data
-
Houston, TX
-
Kriegel H-P, Kröger P, Renz M, Wurst S. A generic framework for efficient subspace clustering of highdimensional data. In: Proceedings of the 5th IEEE International Conference on Data Mining (ICDM), Houston, TX; 2005.
-
(2005)
Proceedings of the 5th IEEE International Conference on Data Mining (ICDM)
-
-
Kriegel, H.-P.1
Kröger, P.2
Renz, M.3
Wurst, S.4
-
53
-
-
67149091034
-
Detection and visualization of subspace cluster hierarchies
-
Bangkok, Thailand
-
Achtert E, Böhm C, Kriegel H-P, Kröger P, Müller-Gorman I, Zimek A. Detection and visualization of subspace cluster hierarchies. In: Proceedings of the 12th International Conference on Database Systems for Advanced Applications (DASFAA), Bangkok, Thailand; 2007.
-
(2007)
Proceedings of the 12th International Conference on Database Systems for Advanced Applications (DASFAA)
-
-
Achtert, E.1
Böhm, C.2
Kriegel, H.-P.3
Kröger, P.4
Müller-Gorman, I.5
Zimek, A.6
-
55
-
-
74549217295
-
Detection of orthogonal concepts in subspaces of high dimensional data
-
Hong Kong, China
-
Günnemann S, Müller E, Färber I, Seidl T. Detection of orthogonal concepts in subspaces of high dimensional data. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management (CIKM), Hong Kong, China; 2009.
-
(2009)
Proceedings of the 18th ACM Conference on Information and Knowledge Management (CIKM)
-
-
Günnemann, S.1
Müller, E.2
Färber, I.3
Seidl, T.4
-
59
-
-
14544300820
-
Computing clusters of correlation connected objects
-
Paris, France
-
Böhm C, Kailing K, Kröger P, Zimek A. Computing clusters of correlation connected objects. In: Proceedings of the ACM International Conference on Management of Data (SIGMOD), Paris, France; 2004.
-
(2004)
Proceedings of the ACM International Conference on Management of Data (SIGMOD)
-
-
Böhm, C.1
Kailing, K.2
Kröger, P.3
Zimek, A.4
-
60
-
-
84873140152
-
A fast algorithm for finding correlation clusters in noise data
-
Nanjing, China
-
Li J, Huang X, Selke C, Yong J. A fast algorithm for finding correlation clusters in noise data. In: Proceedings of the 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Nanjing, China; 2007.
-
(2007)
Proceedings of the 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)
-
-
Li, J.1
Huang, X.2
Selke, C.3
Yong, J.4
-
61
-
-
70449112553
-
Robust, complete, and efficient correlation clustering
-
Minneapolis, MN
-
Achtert E, Böhm C, Kriegel H-P, Kröger P, Zimek A. Robust, complete, and efficient correlation clustering.In: Proceedings of the 7th SIAM International Conference on Data Mining (SDM), Minneapolis, MN; 2007.
-
(2007)
Proceedings of the 7th SIAM International Conference on Data Mining (SDM)
-
-
Achtert, E.1
Böhm, C.2
Kriegel, H.-P.3
Kröger, P.4
Zimek, A.5
-
62
-
-
46649105498
-
On exploring complex relationships of correlation clusters
-
Banff, Canada
-
Achtert E, Böhm C, Kriegel H-P, Kröger P, Zimek A. On exploring complex relationships of correlation clusters. In: Proceedings of the 19th International Conference on Scientific and Statistical Database Management (SSDBM), Banff, Canada; 2007.
-
(2007)
Proceedings of the 19th International Conference on Scientific and Statistical Database Management (SSDBM)
-
-
Achtert, E.1
Böhm, C.2
Kriegel, H.-P.3
Kröger, P.4
Zimek, A.5
-
65
-
-
52649169009
-
Robust clustering in arbitrarily oriented subspaces
-
Atlanta, GA
-
Achtert E, Böhm C, David J, Kröger P, Zimek A. Robust clustering in arbitrarily oriented subspaces. In: Proceedings of the 8th SIAM International Conference on Data Mining (SDM), Atlanta, GA; 2008.
-
(2008)
Proceedings of the 8th SIAM International Conference on Data Mining (SDM)
-
-
Achtert, E.1
Böhm, C.2
David, J.3
Kröger, P.4
Zimek, A.5
-
66
-
-
73849098232
-
Global correlation clustering based on the Hough transform
-
Achtert E, Böhm C, David J, Kröger P, Zimek A. Global correlation clustering based on the Hough transform. Stat Anal Data Min 2008, 1:111-127.
-
(2008)
Stat Anal Data Min
, vol.1
, pp. 111-127
-
-
Achtert, E.1
Böhm, C.2
David, J.3
Kröger, P.4
Zimek, A.5
-
67
-
-
77952784247
-
Finding clusters in subspaces of very large, multidimensional datasets
-
Long Beach, CA
-
Cordeiro RLF, Traina AJM, Faloutsos C, Traina Jr C. Finding clusters in subspaces of very large, multidimensional datasets. In: Proceedings of the 26th International Conference on Data Engineering (ICDE), Long Beach, CA; 2010.
-
(2010)
Proceedings of the 26th International Conference on Data Engineering (ICDE)
-
-
Cordeiro, R.L.F.1
Traina, A.J.M.2
Faloutsos, C.3
Traina Jr, C.4
-
68
-
-
0019574599
-
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography
-
Fischler MA, Bolles RC. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 1981, 24:381-395.
-
(1981)
Commun ACM
, vol.24
, pp. 381-395
-
-
Fischler, M.A.1
Bolles, R.C.2
-
71
-
-
84857993572
-
Correlation clustering
-
Zimek A. Correlation clustering. ACM SIGKDD Explor 2009, 11:53-54.
-
(2009)
ACM SIGKDD Explor
, vol.11
, pp. 53-54
-
-
Zimek, A.1
-
73
-
-
84873198024
-
discovering, summarizing and using multiple clusterings MultiClust
-
Fern XZ, Davidson I, Dy J.G. MultiClust 2010: discovering, summarizing and using multiple clusterings. ACM SIGKDD Explor; 2010, 12:47-49.
-
(2010)
ACM SIGKDD Explor. 2010
, vol.12
, pp. 47-49
-
-
Fern, X.Z.1
Davidson, I.2
Dy, J.G.3
-
74
-
-
84873204449
-
-
editors Athens, Greece: Held in Conjunction with ECML PKDD 2011
-
Müller E, Günnemann S, Assent I, Seidl T, editors. Second MultiClustWorkshop: Discovering, Summarizing and Using Multiple Clusterings, Held in Conjunction with ECML PKDD 2011, Athens, Greece; 2011.
-
(2011)
Second MultiClustWorkshop: Discovering, Summarizing and Using Multiple Clusterings
-
-
Müller, E.1
Günnemann, S.2
Assent, I.3
Seidl, T.4
-
75
-
-
84873186848
-
Subspace clustering, ensemble clustering, alternative clustering, multiview clustering: what can we learn from each other?
-
Washington, DC
-
Kriegel H-P, Zimek A. Subspace clustering, ensemble clustering, alternative clustering, multiview clustering: what can we learn from each other? In: Multi-Clust: First International Workshop on Discovering, Summarizing and Using Multiple Clusterings Held in Conjunction with KDD 2010, Washington, DC; 2010.
-
(2010)
Multi-Clust: First International Workshop on Discovering, Summarizing and Using Multiple Clusterings Held in Conjunction with KDD 2010
-
-
Kriegel, H.-P.1
Zimek, A.2
-
77
-
-
84873172464
-
Less is more: Non-redundant subspace clustering
-
Washington, DC
-
Assent I, Müller E, Günnemann S, Krieger R, Seidl T. Less is more: Non-redundant subspace clustering. In: MultiClust: First InternationalWorkshop on Discovering, Summarizing and Using Multiple Clusterings Held in Conjunction with KDD 2010, Washington, DC; 2010.
-
(2010)
MultiClust: First InternationalWorkshop on Discovering, Summarizing and Using Multiple Clusterings Held in Conjunction with KDD 2010
-
-
Assent, I.1
Müller, E.2
Günnemann, S.3
Krieger, R.4
Seidl, T.5
-
78
-
-
84864264233
-
ASCLU: alternative subspace clustering
-
Washington, DC
-
Günnemann S, Färber I, Müller E, Seidl T. ASCLU: alternative subspace clustering. In: MultiClust: First International Workshop on Discovering, Summarizing and Using Multiple Clusterings Held in Conjunction with KDD 2010, Washington, DC; 2010.
-
(2010)
MultiClust: First International Workshop on Discovering, Summarizing and Using Multiple Clusterings Held in Conjunction with KDD 2010
-
-
Günnemann, S.1
Färber, I.2
Müller, E.3
Seidl, T.4
-
79
-
-
77951149821
-
Relevant subspace clustering: mining the most interesting non-redundant concepts in high dimensional data
-
Miami, FL
-
Müller E, Assent I, Günnemann S, Krieger R, Seidl T. Relevant subspace clustering: mining the most interesting non-redundant concepts in high dimensional data. In: Proceedings of the 9th IEEE International Conference on Data Mining (ICDM), Miami, FL; 2009.
-
(2009)
Proceedings of the 9th IEEE International Conference on Data Mining (ICDM)
-
-
Müller, E.1
Assent, I.2
Günnemann, S.3
Krieger, R.4
Seidl, T.5
-
82
-
-
70049108502
-
Simultaneous unsupervised learning of disparate clusterings
-
Jain P, Meka R, Dhillon IS. Simultaneous unsupervised learning of disparate clusterings. Stat Anal Data Min 2008, 1:195-210.
-
(2008)
Stat Anal Data Min
, vol.1
, pp. 195-210
-
-
Jain, P.1
Meka, R.2
Dhillon, I.S.3
-
85
-
-
84873117260
-
COALA: a novel approach for the extraction of an alternate clustering of high quality and high dissimilarity
-
Hong Kong, China
-
Bae E, Bailey J. COALA: a novel approach for the extraction of an alternate clustering of high quality and high dissimilarity. In: Proceedings of the 6th IEEE International Conference on Data Mining (ICDM), Hong Kong, China; 2006.
-
(2006)
Proceedings of the 6th IEEE International Conference on Data Mining (ICDM)
-
-
Bae, E.1
Bailey, J.2
-
90
-
-
84873151485
-
Generating a diverse set of high-quality clusterings
-
Athens, Greece
-
Phillips JM, Raman P, Venkatasubramanian S. Generating a diverse set of high-quality clusterings. In: Second MultiClust Workshop: Discovering, Summarizing and Using Multiple Clusterings Held in Conjunction with ECML PKDD 2011, Athens, Greece; 2011.
-
(2011)
Second MultiClust Workshop: Discovering, Summarizing and Using Multiple Clusterings Held in Conjunction with ECML PKDD 2011
-
-
Phillips, J.M.1
Raman, P.2
Venkatasubramanian, S.3
-
91
-
-
84862688946
-
Densitybased clustering
-
Kriegel H-P, Kröger P, Sander J, Zimek A. Densitybased clustering. WIREs Data Min Knowl Discov 2011, 1:231-240.
-
(2011)
WIREs Data Min Knowl Discov
, vol.1
, pp. 231-240
-
-
Kriegel, H.-P.1
Kröger, P.2
Sander, J.3
Zimek, A.4
-
92
-
-
72849151744
-
DensEst: density estimation for data mining in high dimensional spaces
-
Sparks, NV
-
Müller E, Assent I, Krieger R, Günnemann S, Seidl T. DensEst: density estimation for data mining in high dimensional spaces. In: Proceedings of the 9th SIAM International Conference on Data Mining (SDM), Sparks, NV; 2009.
-
(2009)
Proceedings of the 9th SIAM International Conference on Data Mining (SDM)
-
-
Müller, E.1
Assent, I.2
Krieger, R.3
Günnemann, S.4
Seidl, T.5
-
93
-
-
84878905116
-
On using class-labels in evaluation of clusterings
-
Washington, DC
-
Färber I, Günnemann S, Kriegel H-P, Kröger P, Müller E, Schubert E, Seidl T, Zimek A. On using class-labels in evaluation of clusterings. In: Multi-Clust: First International Workshop on Discovering, Summarizing and Using Multiple Clusterings Held in Conjunction with KDD 2010, Washington, DC; 2010.
-
(2010)
Multi-Clust: First International Workshop on Discovering, Summarizing and Using Multiple Clusterings Held in Conjunction with KDD 2010
-
-
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
-
95
-
-
84873104728
-
Evaluation of multiple clustering solutions
-
Athens, Greece
-
Kriegel H-P, Schubert E, Zimek A. Evaluation of multiple clustering solutions. In: Second Multi-Clust Workshop: Discovering, Summarizing and Using Multiple Clusterings Held in Conjunction with ECML PKDD 2011, Athens, Greece; 2011.
-
(2011)
Second Multi-Clust Workshop: Discovering, Summarizing and Using Multiple Clusterings Held in Conjunction with ECML PKDD 2011
-
-
Kriegel, H.-P.1
Schubert, E.2
Zimek, A.3
-
96
-
-
83055191163
-
External evaluation measures for subspace clustering
-
Glasgow, UK
-
Günnemann S, Färber I, Müller E, Assent I, Seidl T. External evaluation measures for subspace clustering. In: Proceedings of the 20th ACM Conference on Information and Knowledge Management (CIKM), Glasgow, UK; 2011.
-
(2011)
Proceedings of the 20th ACM Conference on Information and Knowledge Management (CIKM)
-
-
Günnemann, S.1
Färber, I.2
Müller, E.3
Assent, I.4
Seidl, T.5
-
98
-
-
84873208330
-
Mining maximal quasi-bicliques to co-cluster stocks and financial ratios for value investment
-
Hong Kong, China
-
Sim K, Li J, Gopalkrishnan V, Liu G. Mining maximal quasi-bicliques to co-cluster stocks and financial ratios for value investment. In: Proceedings of the 6th IEEE International Conference on Data Mining (ICDM), Hong Kong, China; 2006.
-
(2006)
Proceedings of the 6th IEEE International Conference on Data Mining (ICDM)
-
-
Sim, K.1
Li, J.2
Gopalkrishnan, V.3
Liu, G.4
-
101
-
-
85136074496
-
A framework for projected clustering of high dimensional data streams
-
Toronto, Canada
-
Aggarwal CC, Han J, Wang J, Yu PS. A framework for projected clustering of high dimensional data streams. In: Proceedings of the 30th International Conference on Very Large Data Bases (VLDB), Toronto, Canada; 2004.
-
(2004)
Proceedings of the 30th International Conference on Very Large Data Bases (VLDB)
-
-
Aggarwal, C.C.1
Han, J.2
Wang, J.3
Yu, P.S.4
-
103
-
-
79961204635
-
Density based subspace clustering over dynamic data
-
Portland, OR
-
Kriegel H-P, Kröger P, Ntoutsi I, Zimek A. Density based subspace clustering over dynamic data. In: Proceedings of the 23rd International Conference on Scientific and Statistical Database Management (SSDBM), Portland, OR; 2011.
-
(2011)
Proceedings of the 23rd International Conference on Scientific and Statistical Database Management (SSDBM)
-
-
Kriegel, H.-P.1
Kröger, P.2
Ntoutsi, I.3
Zimek, A.4
-
104
-
-
33845981111
-
CLICKS: an effective algorithm for mining subspace clusters in categorical datasets
-
Zaki MJ, Peters M, Assent I, Seidl T. CLICKS: an effective algorithm for mining subspace clusters in categorical datasets. Data Knowl Eng 2007, 60:51-70.
-
(2007)
Data Knowl Eng
, vol.60
, pp. 51-70
-
-
Zaki, M.J.1
Peters, M.2
Assent, I.3
Seidl, T.4
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