-
1
-
-
54249110594
-
Mixed membership stochastic blockmodels
-
June
-
E. M. Airoldi, D. M. Blei, S. E. Fienberg, and E. P. Xing. Mixed membership stochastic blockmodels. J. Mach. Learn. Res., 9:1981-2014, June 2008.
-
(2008)
J. Mach. Learn. Res.
, vol.9
, pp. 1981-2014
-
-
Airoldi, E.M.1
Blei, D.M.2
Fienberg, S.E.3
Xing, E.P.4
-
2
-
-
33749575022
-
Group formation in large social networks: membership, growth, and evolution
-
L. Backstrom, D. P. Huttenlocher, J. M. Kleinberg, and X. Lan. Group formation in large social networks: membership, growth, and evolution. In KDD, pages 44-54, 2006.
-
(2006)
KDD
, pp. 44-54
-
-
Backstrom, L.1
Huttenlocher, D.P.2
Kleinberg, J.M.3
Lan, X.4
-
3
-
-
12244300524
-
A probabilistic framework for semi-supervised clustering
-
S. Basu, M. Bilenko, and R. J. Mooney. A probabilistic framework for semi-supervised clustering. In KDD, pages 59-68, 2004.
-
(2004)
KDD
, pp. 59-68
-
-
Basu, S.1
Bilenko, M.2
Mooney, R.J.3
-
4
-
-
0003857778
-
A Gentle Tutorial on the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models
-
(ICSI-TR-97-021)
-
J. Bilmes. A Gentle Tutorial on the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models. University of Berkeley Tech Rep ICSITR, (ICSI-TR-97-021), 1997.
-
(1997)
University of Berkeley Tech Rep ICSITR
-
-
Bilmes, J.1
-
5
-
-
77952773506
-
Progressive clustering of networks using structure-connected order of traversal
-
D. Bortner and J. Han. Progressive clustering of networks using structure-connected order of traversal. In ICDE, 653-656, 2010.
-
(2010)
ICDE
, pp. 653-656
-
-
Bortner, D.1
Han, J.2
-
7
-
-
36849005505
-
Evolutionary spectral clustering by incorporating temporal smoothness
-
Y. Chi, X. Song, D. Zhou, K. Hino, and B. L. Tseng. Evolutionary spectral clustering by incorporating temporal smoothness. In KDD, pages 153-162, 2007.
-
(2007)
KDD
, pp. 153-162
-
-
Chi, Y.1
Song, X.2
Zhou, D.3
Hino, K.4
Tseng, B.L.5
-
8
-
-
41349117788
-
Finding community structure in very large networks
-
066111
-
A. Clauset, M. E. J. Newman, and C. Moore. Finding community structure in very large networks. In Phys. Rev. E 70, 066111, 2004.
-
(2004)
Phys. Rev. E
, vol.70
-
-
Clauset, A.1
Newman, M.E.J.2
Moore, C.3
-
9
-
-
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. Journal of the Royal Statistical Society, Series B, 39(1):1-38, 1977.
-
(1977)
Journal of the Royal Statistical Society, Series B
, vol.39
, Issue.1
, pp. 1-38
-
-
Dempster, A.P.1
Laird, N.M.2
Rubin, D.B.3
-
10
-
-
72449181218
-
Heterogeneous source consensus learning via decision propagation and negotiation
-
J. Gao, W. Fan, Y. Sun, and J. Han. Heterogeneous source consensus learning via decision propagation and negotiation. In KDD, 2009.
-
(2009)
KDD
-
-
Gao, J.1
Fan, W.2
Sun, Y.3
Han, J.4
-
11
-
-
85026972772
-
Probabilistic latent semantic analysis
-
T. Hofmann. Probabilistic latent semantic analysis. In UAI, 1999.
-
(1999)
UAI
-
-
Hofmann, T.1
-
12
-
-
33845291376
-
Investigation on several model selection criteria for determining the number of cluster
-
X. Hu and L. Xu. Investigation on several model selection criteria for determining the number of cluster. Neural Inform. Proces. - Lett. and Reviews, 4:1-10, 2004.
-
(2004)
Neural Inform. Proces. - Lett. and Reviews
, vol.4
, pp. 1-10
-
-
Hu, X.1
Xu, L.2
-
13
-
-
0004161991
-
-
Prentice-Hall, Inc., Upper Saddle River, NJ, USA
-
A. K. Jain and R. C. Dubes. Algorithms for clustering data. Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 1988.
-
(1988)
Algorithms for clustering data
-
-
Jain, A.K.1
Dubes, R.C.2
-
14
-
-
79851513672
-
A particle-and-density based evolutionary clustering method for dynamic networks
-
M.-S. Kim and J. Han. A particle-and-density based evolutionary clustering method for dynamic networks. PVLDB, 2(1):622-633, 2009.
-
(2009)
PVLDB
, vol.2
, Issue.1
, pp. 622-633
-
-
Kim, M.-S.1
Han, J.2
-
15
-
-
52949106331
-
Statistical properties of community structure in large social and information networks
-
J. Leskovec, K. J. Lang, A. Dasgupta, and M. W. Mahoney. Statistical properties of community structure in large social and information networks. In WWW, 2008.
-
(2008)
WWW
-
-
Leskovec, J.1
Lang, K.J.2
Dasgupta, A.3
Mahoney, M.W.4
-
16
-
-
36849020504
-
A probabilistic framework for relational clustering
-
B. Long, Z. M. Zhang, and P. S. Yu. A probabilistic framework for relational clustering. In KDD, pages 470-479, 2007.
-
(2007)
KDD
, pp. 470-479
-
-
Long, B.1
Zhang, Z.M.2
Yu, P.S.3
-
17
-
-
34548583274
-
A tutorial on spectral clustering
-
December
-
U. Luxburg. A tutorial on spectral clustering. Statistics and Computing, 17:395-416, December 2007.
-
(2007)
Statistics and Computing
, vol.17
, pp. 395-416
-
-
Luxburg, U.1
-
18
-
-
57349152312
-
Topic modeling with network regularization
-
Q. Mei, D. Cai, D. Zhang, and C. Zhai. Topic modeling with network regularization. In WWW, 2008.
-
(2008)
WWW
-
-
Mei, Q.1
Cai, D.2
Zhang, D.3
Zhai, C.4
-
19
-
-
34250115918
-
An examination of procedures for determining the number of clusters in a data set
-
June
-
G. Milligan and M. Cooper. An examination of procedures for determining the number of clusters in a data set. Psychometrika, 50(2):159-179, June 1985.
-
(1985)
Psychometrika
, vol.50
, Issue.2
, pp. 159-179
-
-
Milligan, G.1
Cooper, M.2
-
20
-
-
36849029834
-
A spectral clustering approach to optimally combining numericalvectors with a modular network
-
M. Shiga, I. Takigawa, and H. Mamitsuka. A spectral clustering approach to optimally combining numericalvectors with a modular network. In KDD, pages 647-656, 2007.
-
(2007)
KDD
, pp. 647-656
-
-
Shiga, M.1
Takigawa, I.2
Mamitsuka, H.3
-
21
-
-
0041965980
-
Cluster ensembles - a knowledge reuse framework for combining multiple partitions
-
March
-
A. Strehl and J. Ghosh. Cluster ensembles - a knowledge reuse framework for combining multiple partitions. J. Mach. Learn. Res., 3:583-617, March 2003.
-
(2003)
J. Mach. Learn. Res.
, vol.3
, pp. 583-617
-
-
Strehl, A.1
Ghosh, J.2
-
22
-
-
77951153812
-
itopicmodel: Information network-integrated topic modeling
-
Y. Sun, J. Han, J. Gao, and Y. Yu. itopicmodel: Information network-integrated topic modeling. In ICDM, pages 493-502, 2009.
-
(2009)
ICDM
, pp. 493-502
-
-
Sun, Y.1
Han, J.2
Gao, J.3
Yu, Y.4
-
23
-
-
70350625449
-
Ranking-based clustering of heterogeneous information networks with star network schema
-
Y. Sun, Y. Yu, and J. Han. Ranking-based clustering of heterogeneous information networks with star network schema. In KDD, 2009.
-
(2009)
KDD
-
-
Sun, Y.1
Yu, Y.2
Han, J.3
-
24
-
-
84880884400
-
Probabilistic classification and clustering in relational data
-
B. Taskar, E. Segal, and D. Koller. Probabilistic classification and clustering in relational data. In IJCAI, pages 870-876, 2001.
-
(2001)
IJCAI
, pp. 870-876
-
-
Taskar, B.1
Segal, E.2
Koller, D.3
-
25
-
-
70350679112
-
Combining link and content for community detection: a discriminative approach
-
T. Yang, R. Jin, Y. Chi, and S. Zhu. Combining link and content for community detection: a discriminative approach. In KDD, 2009.
-
(2009)
KDD
-
-
Yang, T.1
Jin, R.2
Chi, Y.3
Zhu, S.4
-
26
-
-
0013246766
-
Spectral relaxation for k-means
-
C. H. Zha, H. Zha, X. He, C. Ding, H. Simon, and M. Gu. Spectral relaxation for k-means. In NIPS, 2001.
-
(2001)
NIPS
-
-
Zha, C.H.1
Zha, H.2
He, X.3
Ding, C.4
Simon, H.5
Gu, M.6
-
28
-
-
77955045035
-
Graph clustering based on structural/attribute similarities
-
Y. Zhou, H. Cheng, and J. X. Yu. Graph clustering based on structural/attribute similarities. Proc. VLDB Endow., 2(1), 2009.
-
(2009)
Proc. VLDB Endow.
, vol.2
, Issue.1
-
-
Zhou, Y.1
Cheng, H.2
Yu, J.X.3
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