-
1
-
-
13844276694
-
Cluster analysis for gene expression data: a survey
-
Jiang D, Tang C, Zhang A. Cluster analysis for gene expression data: a survey. IEEE Trans Knowl Data Eng 2004, 16:1370-1386.
-
(2004)
IEEE Trans Knowl Data Eng
, vol.16
, pp. 1370-1386
-
-
Jiang, D.1
Tang, C.2
Zhang, A.3
-
2
-
-
33746747476
-
Machine learning in bioinformatics: a brief survey and recommendations for practitioners
-
Bhaskar H, Hoyle D, Singh S. Machine learning in bioinformatics: a brief survey and recommendations for practitioners. Comput Biol Med 2006, 36:1104-1125.
-
(2006)
Comput Biol Med
, vol.36
, pp. 1104-1125
-
-
Bhaskar, H.1
Hoyle, D.2
Singh, S.3
-
3
-
-
9444236233
-
Ranking interesting subspaces for clustering high dimensional data
-
Cavtat-Dubrovnik, Croatia: Springer
-
Kailing K, Kriegel H, Kröger P, Wanka S. Ranking interesting subspaces for clustering high dimensional data. In: Knowledge Discovery in Databases: PKDD 2003. Cavtat-Dubrovnik, Croatia: Springer; 2003, 241-252.
-
(2003)
Knowledge Discovery in Databases: PKDD 2003
, pp. 241-252
-
-
Kailing, K.1
Kriegel, H.2
Kröger, P.3
Wanka, S.4
-
5
-
-
16444383160
-
Survey of clustering algorithms
-
Xu R, Wunsch D. Survey of clustering algorithms. IEEE Trans Neural Netw 2005, 16:645-678.
-
(2005)
IEEE Trans Neural Netw
, vol.16
, pp. 645-678
-
-
Xu, R.1
Wunsch, D.2
-
6
-
-
84892062680
-
A survey of clustering data mining techniques
-
Kogan J, Nicholas C, Teboulle M, eds Heidelberg: Springer
-
Berkhin P. A survey of clustering data mining techniques. In: Kogan J, Nicholas C, Teboulle M, eds. Grouping Multidimensional Data, Recent Advances in Clustering. Heidelberg: Springer; 2006, 25-71.
-
(2006)
Grouping Multidimensional Data, Recent Advances in Clustering
, pp. 25-71
-
-
Berkhin, P.1
-
7
-
-
0032441150
-
Cluster analysis and display of genome-wide expression patterns
-
USA
-
Eisen M, Spellman P, Brown P, Botstein D. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA 1998, 95:14863-14868.
-
(1998)
Proc Natl Acad Sci
, vol.95
, pp. 14863-14868
-
-
Eisen, M.1
Spellman, P.2
Brown, P.3
Botstein, D.4
-
10
-
-
28444457322
-
Lifting the curse of dimensionality
-
Kuo F, Sloan I. Lifting the curse of dimensionality. Notices Am Math Soc 2005, 52:1320.
-
(2005)
Notices Am Math Soc
, vol.52
, pp. 1320
-
-
Kuo, F.1
Sloan, I.2
-
11
-
-
84947205653
-
When is nearest neighbor meaningful?
-
Beeri C, Buneman P, eds Berlin/Heidelberg: Springer
-
Beyer K, Goldstein J, Ramakrishnan R, Shaft U. When is nearest neighbor meaningful? In: Beeri C, Buneman P, eds. Database Theory ICDT99. Lecture Notes in Computer Science. Vol. 1540. Berlin/Heidelberg: Springer; 1999, 217-235.
-
(1999)
Database Theory ICDT99. Lecture Notes in Computer Science
, vol.1540
, pp. 217-235
-
-
Beyer, K.1
Goldstein, J.2
Ramakrishnan, R.3
Shaft, U.4
-
15
-
-
84949479246
-
On the surprising behavior of distance metrics in high dimensional space
-
London, UK: Springer
-
Aggarwal C, Hinneburg A, Keim D. On the surprising behavior of distance metrics in high dimensional space. In: Database Theory ICDT 2001. London, UK: Springer; 2001, 420-434.
-
(2001)
Database Theory ICDT 2001
, pp. 420-434
-
-
Aggarwal, C.1
Hinneburg, A.2
Keim, D.3
-
16
-
-
85170282443
-
A densitybased algorithm for discovering clusters in large spatial databases with noise
-
Portland, Oregon: AAAI Press
-
Ester M, Kriegel H, Sander J, Xu X. A densitybased algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Vol. 96. Portland, Oregon: AAAI Press; 1996, 226-231.
-
(1996)
Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining
, vol.96
, pp. 226-231
-
-
Ester, M.1
Kriegel, H.2
Sander, J.3
Xu, X.4
-
17
-
-
84862688946
-
Densitybased clustering
-
Kriegel HP, Kröger P, Sander J, Zimek A. Densitybased clustering. Wiley Interdiscip Rev: Data Min Knowl Discov 2011, 1:231-240.
-
(2011)
Wiley Interdiscip Rev: Data Min Knowl Discov
, vol.1
, pp. 231-240
-
-
Kriegel, H.P.1
Kröger, P.2
Sander, J.3
Zimek, A.4
-
18
-
-
47249137675
-
DUSC: dimensionality unbiased subspace clustering
-
Omaha, Nebraska: IEEE
-
Assent I, Krieger R, Müller E, Seidl T. DUSC: dimensionality unbiased subspace clustering. In: Seventh IEEE International Conference on Data Mining. ICDM 2007. Omaha, Nebraska: IEEE; 2008, 409-414.
-
(2008)
Seventh IEEE International Conference on Data Mining. ICDM 2007
, pp. 409-414
-
-
Assent, I.1
Krieger, R.2
Müller, E.3
Seidl, T.4
-
20
-
-
0003260456
-
Density estimation for statistics and data analysis
-
New York: Chapman and Hall
-
Silverman B. Density estimation for statistics and data analysis. In: Monographs on Statistics and Applied Probability. New York: Chapman and Hall; 1986.
-
(1986)
Monographs on Statistics and Applied Probability
-
-
Silverman, B.1
-
22
-
-
84866454507
-
Distance-preserving dimensionality reduction
-
Yang L. Distance-preserving dimensionality reduction. Wiley Interdiscip Rev: Data Min Knowl Discov 2011, 1:369-380. [http://dx.doi.org/10.1002/widm.39].
-
(2011)
Wiley Interdiscip Rev: Data Min Knowl Discov
, vol.1
, pp. 369-380
-
-
Yang, L.1
-
23
-
-
78149305852
-
Adaptive dimension reduction for clustering high dimensional data
-
Maebashi City, Japan: IEEE Computer Society
-
Ding C, He X, Zha H, Simon H. Adaptive dimension reduction for clustering high dimensional data. In: Proceedings of the 2002 IEEE International Conference on Data Mining. Maebashi City, Japan: IEEE Computer Society; 2002, 147.
-
(2002)
Proceedings of the 2002 IEEE International Conference on Data Mining
, pp. 147
-
-
Ding, C.1
He, X.2
Zha, H.3
Simon, H.4
-
24
-
-
1942517297
-
Random projection for high dimensional data clustering: a cluster ensemble approach
-
Fawcett T, MishraN, eds Menlo Park, CA: AAAI Press
-
Fern X, Brodley C. Random projection for high dimensional data clustering: a cluster ensemble approach. In: Fawcett T, MishraN, eds. The Twentieth International Conference on Machine Learning. Menlo Park, CA: AAAI Press; 2003.
-
(2003)
The Twentieth International Conference on Machine Learning
-
-
Fern, X.1
Brodley, C.2
-
25
-
-
17044405923
-
Toward integrating feature selection algorithms for classification and clustering
-
Liu H, Yu L. Toward integrating feature selection algorithms for classification and clustering. IEEE Trans Knowl Data Eng 2005, 17:491-502.
-
(2005)
IEEE Trans Knowl Data Eng
, vol.17
, pp. 491-502
-
-
Liu, H.1
Yu, L.2
-
26
-
-
0032090765
-
Automatic subspace clustering of high dimensional data for data mining applications
-
Agrawal R, Gehrke J, Gunopulos D, Raghavan P. Automatic subspace clustering of high dimensional data for data mining applications. ACM SIGMOD Record 1998, 27:94-105.
-
(1998)
ACM SIGMOD Record
, vol.27
, pp. 94-105
-
-
Agrawal, R.1
Gehrke, J.2
Gunopulos, D.3
Raghavan, P.4
-
27
-
-
67149084291
-
Clustering highdimensional data: a survey on subspace clustering, pattern-based clustering, and correlation clustering
-
Kriegel H, Kröger P, Zimek A. Clustering highdimensional 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.1
Kröger, P.2
Zimek, A.3
-
28
-
-
84865086248
-
Evaluating clustering in subspace projections of high dimensional data
-
Müller E, Günnemann S, Assent I, Seidl T. Evaluating clustering in subspace projections of high dimensional data. PVLDB 2009, 2:1270-1281.
-
(2009)
PVLDB
, vol.2
, pp. 1270-1281
-
-
Müller, E.1
Günnemann, S.2
Assent, I.3
Seidl, T.4
-
29
-
-
71949123741
-
Subspace and projected clustering: experimental evaluation and analysis
-
Moise G, Zimek A, Kröger P, Kriegel H, 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.4
Sander, J.5
-
30
-
-
17044376078
-
Subspace clustering for high dimensional data: a review
-
Parsons L, Haque E, Liu H. Subspace clustering for high dimensional data: a review. ACM SIGKDD Explor Newslett 2004, 6:90-105.
-
(2004)
ACM SIGKDD Explor Newslett
, vol.6
, pp. 90-105
-
-
Parsons, L.1
Haque, E.2
Liu, H.3
-
31
-
-
33847338032
-
Locally adaptive metrics for clustering high dimensional data
-
Domeniconi C, Gunopulos D, Ma S, Yan B, Al Razgan M, 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
-
32
-
-
0002629270
-
Maximum likelihood from incomplete data via the EM algorithm
-
Dempster A, Laird N, Rubin D. Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc B 1977, 39:1-38.
-
(1977)
J R Stat Soc B
, vol.39
, pp. 1-38
-
-
Dempster, A.1
Laird, N.2
Rubin, D.3
-
34
-
-
0347172110
-
OPTICS: ordering points to identify the clustering structure
-
Philadelphia, PA: ACM
-
Ankerst M, Breunig M, Kriegel H, Sander J. OPTICS: ordering points to identify the clustering structure. In: Proceedings of the ACM SIGMOD International Conference on Management of Data. Philadelphia, PA: ACM; 1999, 49-60.
-
(1999)
Proceedings of the ACM SIGMOD International Conference on Management of Data
, pp. 49-60
-
-
Ankerst, M.1
Breunig, M.2
Kriegel, H.3
Sander, J.4
-
35
-
-
3142768191
-
Biclustering algorithms for biological data analysis: a survey
-
Madeira S, Oliveira A. Biclustering algorithms for biological data analysis: a survey. IEEE/ACM Trans Computat Biol Bioinf 2004, 1:24-45.
-
(2004)
IEEE/ACM Trans Computat Biol Bioinf
, vol.1
, pp. 24-45
-
-
Madeira, S.1
Oliveira, A.2
-
39
-
-
0000835955
-
Optimal grid-clustering: towards breaking the curse of dimensionality in highdimensional clustering
-
Edinburgh, Scotland: Morgan Kaufmann
-
Hinneburg A, Keim D. Optimal grid-clustering: towards breaking the curse of dimensionality in highdimensional clustering. In: Proceedings of the 25th International Conference on Very Large Data Bases. Edinburgh, Scotland: Morgan Kaufmann. 1999, 506-517.
-
(1999)
Proceedings of the 25th International Conference on Very Large Data Bases
, pp. 506-517
-
-
Hinneburg, A.1
Keim, D.2
-
41
-
-
25144451767
-
The curse of dimensionality in data mining and time series prediction
-
Cabestany J, Prieto A, Hernández FS, eds Heidelberg: Springer
-
Verleysen M, François D. The curse of dimensionality in data mining and time series prediction. In: Cabestany J, Prieto A, Hernández FS, eds. Computational Intelligence and Bioinspired Systems. Lecture Notes in Computer Science. Heidelberg: Springer; 2005, 758-770.
-
(2005)
Computational Intelligence and Bioinspired Systems. Lecture Notes in Computer Science
, pp. 758-770
-
-
Verleysen, M.1
François, D.2
-
42
-
-
0042711018
-
On the need for time series data mining benchmarks: a survey and empirical demonstration
-
Keogh E, Kasetty S. On the need for time series data mining benchmarks: a survey and empirical demonstration. Data Min Knowl Discov 2003, 7:349-371.
-
(2003)
Data Min Knowl Discov
, vol.7
, pp. 349-371
-
-
Keogh, E.1
Kasetty, S.2
-
43
-
-
24044470614
-
Clustering of time series data-a survey
-
Liao W. Clustering of time series data-a survey. Pattern Recognit 2005, 38:1857-1874.
-
(2005)
Pattern Recognit
, vol.38
, pp. 1857-1874
-
-
Liao, W.1
-
44
-
-
34548093287
-
Experiencing SAX: a novel symbolic representation of time series
-
Lin J, Keogh E, Wei L, Lonardi S. Experiencing SAX: a novel symbolic representation of time series. Data Min Knowl Discov 2007, 15:107-144.
-
(2007)
Data Min Knowl Discov
, vol.15
, pp. 107-144
-
-
Lin, J.1
Keogh, E.2
Wei, L.3
Lonardi, S.4
-
45
-
-
3543085722
-
Empirical and theoretical comparisons of selected criterion functions for document clustering
-
Zhao Y, Karypis G. Empirical and theoretical comparisons of selected criterion functions for document clustering. Mach Learn 2004, 55:311-331.
-
(2004)
Mach Learn
, vol.55
, pp. 311-331
-
-
Zhao, Y.1
Karypis, G.2
-
46
-
-
79951813437
-
The challenges of clustering high-dimensional data
-
Wille LT, ed. Heidelberg: Springer
-
Steinbach M, Ertöz L, Kumar V. The challenges of clustering high-dimensional data. In: Wille LT, ed., New Directions in Statistical Physics: Bioinformatics and Pattern Recognition. Heidelberg: Springer; 2003, 273-307.
-
(2003)
New Directions in Statistical Physics: Bioinformatics and Pattern Recognition
, pp. 273-307
-
-
Steinbach, M.1
Ertöz, L.2
Kumar, V.3
-
47
-
-
80055063556
-
Distance metrics for high dimensional nearest neighborhood recovery: compression and normalization
-
France SL, Carroll JD, Xiong H. Distance metrics for high dimensional nearest neighborhood recovery: compression and normalization. Inf Sci 2012, 184:92-110.
-
(2012)
Inf Sci
, vol.184
, pp. 92-110
-
-
France, S.L.1
Carroll, J.D.2
Xiong, H.3
-
48
-
-
30344483178
-
Document clustering using locality preserving indexing
-
Cai D, He X, Han J. Document clustering using locality preserving indexing. IEEE Trans Knowl Data Eng 2005, 17:1624-1637.
-
(2005)
IEEE Trans Knowl Data Eng
, vol.17
, pp. 1624-1637
-
-
Cai, D.1
He, X.2
Han, J.3
-
51
-
-
34548025132
-
A survey of kernel and spectral methods for clustering
-
Filippone M, Camastra F, Masulli F, Rovetta S. A survey of kernel and spectral methods for clustering. Pattern Recognit 2008, 41:176-190.
-
(2008)
Pattern Recognit
, vol.41
, pp. 176-190
-
-
Filippone, M.1
Camastra, F.2
Masulli, F.3
Rovetta, S.4
-
52
-
-
57849122848
-
-
Technical Report TR-07-35, Department of Computer Science, Virginia Tech. Blacksburg, VA
-
Andrews N.O., Fox E.A. Recent developments in document clustering. Technical Report TR-07-35, Department of Computer Science, Virginia Tech. Blacksburg, VA; 2007.
-
(2007)
Recent developments in document clustering
-
-
Andrews, N.O.1
Fox, E.A.2
-
53
-
-
2542587466
-
Relationship-based clustering and visualization for high-dimensional data mining
-
Strehl A, Ghosh J. Relationship-based clustering and visualization for high-dimensional data mining. INFORMS J Comput 2003, 15:208-230.
-
(2003)
INFORMS J Comput
, vol.15
, pp. 208-230
-
-
Strehl, A.1
Ghosh, J.2
|