References
1 Jain, A,Dubes, R.Algorithms for Clustering Data.Upper Saddle River, NJ:
Prentice‐Hall; 1998.
2 Jain, AK,Murty, MN,Flynn, PJ.Data clustering: a review.ACM Comput Surv1999,31:264–323.
3 Blei, DM,Ng, AY,Jordan, MI.Latent Dirichlet allocation.J Mach Learn Res2003,3:993–1022.
4 Dave, RN,Krishnapuram, R. Robust clustering methods: a unified view.IEEE Trans Fuzzy Syst1997,5:270–293.
5 Kim, J,Krishnapuram, R,Dave, R.Application of the least trimmed squares technique to prototype‐based clustering.Pattern Recognit Lett1996,17:633–641.
6 Wu, K‐L,Yang, M‐S.Alternative
c‐means clustering algorithms.Pattern Recognit2002,35:2267–2278.
7 Banerjee, A,Merugu, S,Dhillon, IS,Ghosh, J.Clustering with Bregman divergences.J Mach Learn Res2005,6:1705–1749.
8 MacQueen, J.Some methods for classification and analysis of multivariate observations. In:
Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability. Los Angeles, CA: University of California Press; 1967, 281–297.
9 Jain, AK.Data clustering: 50 years beyond
k‐means.Pattern Recognit Lett2010,31:651–666. Award winning papers from the 19th International Conference on Pattern Recognition (ICPR).
10 Steinbach, M,Karypis, G,Kumar, V.A comparison of document clustering techniques. In:
The Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Boston, MA; August 20–23, 2000.
11 Redmond, SJ,Heneghan, C.A method for initialising the
k‐means clustering algorithm using kd‐trees.Pattern Recognit Lett2007,28:965–973.
12 He, J,Lan, M,Tan, C‐L,Sung, S‐Y,Low, H‐B.Initialization of cluster refinement algorithms: a review and comparative study. In:
2004 IEEE International Joint Conference on Neural Networks; 2004, 1–4:(xlvii+3302).
13 Hall, LO,Ozyurt, IB,Bezdek, JC.Clustering with a genetically optimized approach.IEEE Trans Evolut Comput1999,3:103–112.
14 Selim, SZ,Ismail, MA.
K‐means‐type algorithms: A generalized convergence theorem and characterization of local optimality.
IEEE Trans Pattern Anal Mach Intell 1984, PAMI‐6(1):81–87.
15 Witten, IH,Frank, E.Data Mining: Practical Machine Learning Tools and Techniques.
2nd ed.San Francisco:
Morgan Kaufmann; 2005.
16 Bezdek, JC,Keller, JM,Krishnapuram, R,Kuncheva, LI,Pal, NR.Will the real iris data please stand up?IEEE Trans Fuzzy Syst1999,7:368–369.
17 Kothari, R,Pitts, D.On finding the number of clusters.Pattern Recognit Lett1999,20:405–416.
18 Kandel, A.Fuzzy Mathematical Techniques With Applications.Boston, MA:
Addison‐Wesley;1986.
19 Wu, K‐L.Analysis of parameter selections for fuzzy
c‐means.
Pattern Recognit 2012, 45:407–415.
http://dx.doi.org/10.1016/j.patcog.2011.07.0120 Yu, J,Cheng, Q,Huang, H.Analysis of the weighting exponent in the FCM.Syst Man Cybern, Part B: Cybern2004,34:634–639.
21 Bezdek, J,Hathaway, R,Sobin, M,Tucker, W.Convergence theory for fuzzy
c‐means: counterexamples and repairs.IEEE Trans Syst Man Cybern1987,17:873–877.
22 Gath, I,Geva, AB.Unsupervised optimal fuzzy clustering.IEEE Trans Pattern Anal Mach Intell1989,11:773–780.
23 Gustafson, DE,Kessel, WC.Fuzzy clustering with a fuzzy covariance matrix. In:
Proc IEEE CDC 1979, 761–766.
24 Dempster, AP,Laird, NM,Rubin, DB.Maximum likelihood from incomplete data via the em algorithm.J R Stat Soc Ser B1997,39:1–38.
25 Wu, CFJ.On the convergence properties of the EM algorithm.Ann Stat1983,11:95–103.
26 Fraley, C,Raferty, AE.How many clusters? Which clustering method? Answers via model‐based cluster analysis.Comput J1998,41:579–588.
27 Krishnapuram, R,Keller, JM.A possibilistic approach to clustering.IEEE Trans Fuzzy Syst1993,1:98–110.
28 Krishnapuram, R,Keller, JM.The possibilistic
c‐means algorithm: insights and recommendations.IEEE Trans Fuzzy Syst1996,4:385–393.
29 Barni, M,Cappellini, V,Mecocci, A.Comments on a possibilistic approach to clustering.IEEE Trans Fuzzy Syst1996,4:393–396.
30 Dubois, D,Prade, H.Possibility theory, probability theory and multiple‐valued logics: a clarification.Ann Math Artif Intell2001,32:35–66.
31 Sung K‐K,Poggio, T.Example‐based learning for view‐based human face detection.IEEE Trans Pattern Anal Mach Intell1998,20:39–51.
32 Krishnapuram, R,Nasraoui, O,Frigui, H.The fuzzy c spherical shells algorithm: a new approach.IEEE Trans Neural Netw1992,3:663–671.
33 Yang, M‐S,Wu, K‐L.Unsupervised possibilistic clustering.Pattern Recognit2006,39:5–21.
34 Timm, H,Borgelt, C,Doring, C,Kruse, R.An extension to possibilistic fuzzy cluster analysis.Fuzzy Sets Syst2004,147:3–16.
35 Ghosh, J,Acharya, A.Cluster ensembles.WIREs Data Min Knowl Discov2001,1:305–315.
36 Strehl, A,Ghosh, J,Cardie, C.Cluster ensembles—a knowledge reuse framework for combining multiple partitions.J Mach Learn Res2002,3:583–617.
37 Schaeffer, SE.Graph clustering.Comput Sci Rev2007,1:27–64.
38 Hathaway, RJ,Davenport, JW,Bezdek, JC.Relational duals of the
c‐means clustering algorithms.Pattern Recognit1989,22:205–212.
39 Krishnapuram, R,Joshi, A,Nasraoui, O,Yi, L.Low‐complexity fuzzy relational clustering algorithms for web mining.IEEE Trans Fuzzy Syst2001,9:595–607.
40 Aggarwal, C,Hinneburg, A,Keim, D.On the surprising behavior of distance metrics in high dimensional space. Lecture Notes in Computer Science. Springer, 2001, 420–434.
41 Banfield, JD,Raftery, AE.Model‐based Gaussian and non‐Gaussian clustering.Biometrics1993,49:803–821.
42 Hofmann, T,Schölkopf, B,Smola, AJ.Kernel methods in machine learning.Ann Stat2008,36:1171–1220.
43 Burges, CJC.A tutorial on support vector machines for pattern recognition.Data Min Knowl Discov1998,2:121–167.
44 Kim, D‐W,Lee, KY,Lee, DH,Lee, KH.Evaluation of the performance of clustering algorithms in kernel‐induced feature space.Pattern Recognit2005,38:607–611.
45 Heo, G,Gader, P.An extension of global fuzzy
c‐means using kernel methods. In:
2010 IEEE International Conference on Fuzzy Systems (FUZZ); 2010, 1–6.
46 Chen, L,Chen, CLP,Lu, M.A multiple‐kernel fuzzy
c‐means algorithm for image segmentation.IEEE Trans Syst Man Cybern, Part B: Cybern2011,99:1–12.
47 Bezdek, JC,Pal, NR.Some new indexes of cluster validity.IEEE Trans Syst Man Cybern, Part B: Cybern1998,28:301–315.
48 Wang, J‐S,Chiang, J‐C.A cluster validity measure with outlier detection for support vector clustering.IEEE Trans Syst Man Cybern, Part B: Cybern2008,38:78–89.
49 Pal, NR,Bezdek, JC.On cluster validity for the fuzzy
c‐means model.Fuzzy Systems, IEEE Transactions on1995, 3(
3):370–379.
50 Kaufman, L,Rousseeuw, P.Finding Groups in Data.New York:
John Wiley %26 Sons;1990.
51 Campello, RJGB,Hruschka, ER.A fuzzy extension of the silhouette width criterion for cluster analysis.Fuzzy Sets Syst2006,157:2858–2875.
52 Vendramin, L,Campello, RJGB,Hruschka, ER.Relative clustering validity criteria: a comparative overview.Stat Anal Data Min2010,3:209–235.
53 Xie, XL,Beni, G.A validity measure for fuzzy clustering.IEEE Trans Pattern Anal Mach Intell1991,13:841–847.
54 Pal, NR,Bezdek, JC.Correction to “on cluster validity for the fuzzy
c‐means model” [correspondence].IEEE Trans Fuzzy Syst1997,5:152–153.
55 Bezdek, J,Hathaway, R.Vat: a tool for visual assessment of (cluster) tendency. In:
Proceedings of International Joint Conference on Neural Networks 2002, 2225–2230.
56 Bezdek, J,Hathaway, R,Huband, J.Visual assessment of clustering tendency for rectangular dissimilarity matrices.IEEE Trans Fuzzy Syst2007,15:890–903.
57 Sedgewick, R,Flajolet, P.An Introduction to the Analysis of Algorithms.Boston, MA:
Addison‐Wesley;1995.
58 Kargupta, H,Huang, W,Sivakumar, K,Johnson, E.Distributed clustering using collective principal component analysis.Knowl Inf Syst2001,3:422–448.
59 Kriegel H‐P,Krieger, P,Pryakhin, A,Schubert, M.Effective and efficient distributed model‐based clustering.IEEE Int Conf Data Min2005,0:258–265.
60 Olman, V,Mao, F,Wu, H,Xu, Y.Parallel clustering algorithm for large data sets with applications in bioinformatics.IEEE/ACM Trans Comput Biol Bioinf2009,6:344–352.
61 Bradley, PS,Fayyad, U,Reina, C.Scaling clustering algorithms to large databases. In:
Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining 1998, 9–15.
62 Hore, P,Hall, LO,Goldgof, DB.Single pass fuzzy
c means. In:
IEEE International Fuzzy Systems Conference, FUZZ IEEE 2007. IEEE; 2007, 1–7.
63 Hore, P,Hall, L,Goldgof, D,Gu, Y,Maudsley, A,Darkazanli, A.A scalable framework for segmenting magnetic resonance images.J Sig Process Syst2009,54:183–203.
64 Eschrich, S,Ke, J,Hall, LO,Goldgof, DB.Fast accurate fuzzy clustering through data reduction.IEEE Trans Fuzzy Syst2003,11:262–270.
65 Hore, P,Hall, LO,Goldgof, DB,Gu, Y.Scalable clustering code. Available at: . (Accessed April 26, 2012).
66 D`Urso, P.Fuzzy clustering for data time arrays with inlier and outlier time trajectories.IEEE Trans Fuzzy Syst2005,13:583–604.
67 Coppi, R,D`Urso, P.Fuzzy unsupervised classification of multivariate time trajectories with the Shannon entropy regularization.Comput Stat Data Anal2006,50:1452–1477.
68 Liao, TW.Clustering of time series dataäîa survey.Pattern Recognit2005,38:1857–1874.
69 Zhang, T,Ramakrishnan, R,Livny, M.Birch: an efficient data clustering method for very large databases. In:
Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, SIGMOD `96. New York: ACM; 1996, 103–114.
70 Guha, S,Rastogi, R,Shim, K.Cure: an efficient clustering algorithm for large databases. In:
Proceedings of ACM SIGMOD International Conference on Management of Data; 1998, 73–74.
71 Aggarwal, CC,Han, J,Wang, J,Yu, PS.A framework for clustering evolving data streams. In:
Proceedings of the International Conference on Very Large Data Bases; 2003.
72 Gupta, C,Grossman, R.Genic: a single pass generalized incremental algorithm for clustering. In:
Proceedings of the Fourth SIAM International Conference on Data Mining (SDM 04), 2004, 22–24. In: den Bussche JV, Vianu V, eds.
Database Theory— ICDT 2001, volume 1973 of Lecture Notes in Computer Science, Vol. 1973. Berlin/Heidelberg: Springer; 2001, 420–434.
73 Dhillon, IS,Mallela, S,Kumar, R.A divisive information theoretic feature clustering algorithm for text classification.J Mach Learn Res2003,3:1265–1287.
74 Linde, Y,Buzo, A,Gray, R.An algorithm for vector quantizer design.IEEE Trans Commun1980,28:84–95.