Conf. on Data Mining, pp. 606–610.
[36]Drineas, P., Frieze, A., Kannan, R., Vempala, S., Vinay, V., 1999. Clustering large
graphs via the singular value decomposition. Machine Learn. 56 (1–3), 9–33. [37]Dubes, Richard C., Jain, Anil K., 1976. Clustering techniques: User’s dilemma.
Pattern Recognition, 247–260.
[38]Duda, R., Hart, P., Stork, D., 2001. Pattern Classification, second ed. John Wiley
and Sons, New York.
[39]Dunn, J.C., 1973. A fuzzy relative of the ISODATA process and its use in
detecting compact well-separated clusters. J. Cybernet. 3, 32–57.
[40]Eschrich, S., Ke, Jingwei, Hall, L.O., Goldgof, D.B., 2003. Fast accurate fuzzy
clustering through data reduction. IEEE Trans. Fuzzy Systems 11 (2), 262–270. [41]Ester, Martin, Peter Kriegel, Hans, S., J?rg, Xu, Xiaowei, 1996. A density-based
algorithm for discovering clusters in large spatial databases with noise. In: Proc. 2nd KDD, AAAI Press.
[42]Ferguson, Thomas S., 1973. A Bayesian analysis of some nonparametric problems.
Ann. Statist. 1, 209–230.
[43]Figueiredo, Mario, Jain, Anil K., 2002. Unsupervised learning of finite mixture
models. IEEE Trans. Pattern Anal. Machine Intell. 24 (3), 381–396.
[44]Figueiredo, M.A.T., Chang, D.S., Murino, V., 2006. Clustering under prior
knowledge with application to image segmentation. Adv. Neural Inform. Process. Systems 19, 401–408.
[45]Fisher, Douglas H., 1987. Knowledge acquisition via incremental conceptual
clustering. Machine Learn., 139–172.
[46]Fisher, L., vanNess, J., 1971. Admissible clustering procedures. Biometrika. Forgy, E.W., 1965. Cluster analysis of multivariate data: Efficiency vs. interpretability
of classifications. Biometrics 21, 768–769.
[47]Frank, Ildiko E., Todeschini, Roberto, 1994. Data Analysis Handbook. Elsevier
Science Inc., pp. 227–228.
[48]Fred, A., Jain, A.K., 2002. Data clustering using evidence accumulation. In: Proc.
Internat. Conf. Pattern Recognition (ICPR).
[49]Frigui, H., Krishnapuram, R., 1999. A robust competitive clustering algorithm
with applications in computer vision. IEEE Trans. Pattern Anal. Machine Intell. 21, 450–465.
[50]Gantz, John F., 2008. The diverse and exploding digital universe. Available
onlineat:
[51]Google Scholar, 2009 (February). Google Scholar.
[52]Guha, Sudipto, Rastogi, Rajeev, Shim, Kyuseok, 1998. CURE: An efficient clustering algorithm for large databases. In: Proc. ICDM., pp. 73–84.
[53]Guha, Sudipto, Rastogi, Rajeev, Shim, Kyuseok, 2000. Rock: A robust clustering
algorithm for categorical attributes. Inform. Systems 25 (5), 345–366.
[54]Guha, Sudipto, Meyerson, A., Mishra, Nina, Motwani, Rajeev, O’Callaghan, L.,
2003a. Clustering data streams: Theory and practice. Trans. Knowledge Discovery Eng.
[55]Guha, Sudipto, Mishra, Nina, Motwani, Rajeev, 2003b. Clustering data streams.
IEEE Trans. Knowledge Data Eng. 15 (3), 515–528.
[56]Hagen, L., Kahng, A.B., 1992. New spectral methods for ratio cut partitioning and
clustering. IEEE Trans. Comput.-Aid. Des. Integrated Circuits Systems 11 (9), 1074–1085.
[57]Han, Jiawei, Kamber, Micheline, 2000. Data Mining: Concepts and Techniques.
Morgan Kaufmann.
[58]Hansen, Mark H., Yu, Bin, 2001. Model selection and the principle of minimum
description length. J. Amer. Statist. Assoc. 96 (454), 746–774.
[59]Har-peled, Sariel, Mazumdar, Soham, 2004. Coresets for k-means and k-median
clustering and their applications. In: Proc. 36th Annu. ACM Sympos. Theory Comput., pp. 291–300.
[60]Hartigan, J.A., 1972. Direct clustering of a data matrix. J. Amer. Statist. Assoc. 67
(337), 123–132.
[61]Hartigan, J.A., 1975. Clustering Algorithms. John Wiley and Sons.Hofmann, T.,
Buhmann, J.M., 1997. Pairwise data clustering by deterministic annealing. IEEE
Trans. Pattern Anal. Machine Intell. 19 (1), 1–14.
[62]Hore, Prodip, Hall, Lawrence O., Goldgof, Dmitry B., 2009a. A scalable
framework for cluster ensembles. Pattern Recognition 42 (5), 676–688.
[63]Hore, Prodip, Hall, Lawrence O., Goldgof, Dmitry B., Gu, Yuhua, Maudsley,
Andrew A., Darkazanli, Ammar, 2009b. A scalable framework for segmenting magnetic resonance images. J. Signal Process. Systems 54 (1–3), 183–203. [64]Hotho, A., Staab, S., Stumme, G., 2003. Ontologies to improve text document
clustering. In: Proc. of the ICDM.
[65]Hu, J., Ray, B.K., Singh, M., 2007. Statistical methods for automated generation
of service engagement staffing plans. IBM J. Res. Dev. 51 (3), 281–293. [66]Iwayama, M., Tokunaga, T., 1995. Cluster-based text categorization: A
comparison of category search strategies. In: Proc. 18th ACM Internat. Conf. on Research and Development in Information Retrieval, pp. 273–281.
[67]Jain, Anil K., Dubes, Richard C., 1988. Algorithms for Clustering Data. Prentice
Hall. Jain, Anil K., Flynn, P., 1996. Image segmentation using clustering. In: Advances in Image Understanding. IEEE Computer Society Press, pp. 65–83. [68]Jain, A.K., Topchy, A., Law, M.H.C., Buhmann, J.M., 2004. Landscape of
clustering algorithms. In: Proc. Internat. Conf. on Pattern Recognition, vol. 1, pp. 260–263.
[69]JSTOR, 2009. JSTOR.
[70]Karypis, George, Kumar, Vipin, 1995. A fast and high quality multilevel scheme
for partitioning irregular graphs. In: Proc. Internat. Conf. on Parallel Processing, pp. 113–122.
[71]Kashima, H., Tsuda, K., Inokuchi, A., 2003. Marginalized Kernels between
labeled graphs. In: Proc. 20th Internat. Conf. on Machine Learning, pp. 321–328. [72]Kashima, H., Hu, J., Ray, B., Singh, M., 2008. K-means clustering of proportional
data using L1 distance. In: Proc. Internat. Conf. on Pattern Recognition, pp. 1–4. [73]Kaufman, Leonard, Rousseeuw, Peter J., 2005. Finding groups in data: An
introduction to cluster analysis.
Wiley series
in Probability and
Statistics.Kleinberg, Jon, 2002. An impossibility theorem for clustering. In: NIPS
15. pp. 463– 470.
[74]Kollios, G., Gunopulos, D., Koudas, N., Berchtold, S., 2003. Efficient biased
sampling for approximate clustering and outlier detection in large data sets. IEEE Trans. Knowledge Data Eng. 15 (5), 1170–1187.
[75]Lange, Tilman, Roth, Volker, Braun, Mikio L., Buhmann, Joachim M., 2004.
Stability-based validation of clustering solutions. Neural Comput. 16 (6), 1299–1323.
[76]Lange, T., Law, M.H., Jain, A.K., Buhmann, J., 2005. Learning with constrained
and unlabelled data. IEEE Comput. Soc. Conf. Comput. Vision Pattern Recognition 1, 730–737.
[77]Law, Martin, Topchy, Alexander, Jain, A.K., 2005. Model-based clustering with
probabilistic constraints.In: Proc. SIAM Conf. on Data Mining, pp. 641–645. [78]Lee, Jung-Eun, Jain, Anil K., Jin, Rong, 2008. Scars, marks and tattoos (SMT):
Soft biometric for suspect and victim identification. In: Proceedings of the Biometric Symposium.Li, W., McCallum, A., 2006. Pachinko allocation: Dag-structured mixture models of topic correlations. In: Proc. 23rd Internat. Conf. on Machine Learning, pp. 577– 584.
[79]Linde, Y., Buzo, A., Gray, R., 1980. An algorithm for vector quantizer design.
IEEE Trans. Comm. 28, 84–94.
[80]Liu, J., Wang, W., Yang, J., 2004. A framework for ontology-driven subspace
clustering. In: Proc. KDD.
[81]Liu, Yi, Jin, Rong, Jain, A.K., 2007. Boostcluster: Boosting clustering by pairwise
constraints. In: Proc. 13th KDD, pp. 450–459.
[82]Lloyd, S., 1982. Least squares quantization in PCM. IEEE Trans. Inform. Theory
28, 129–137.
[83]Originally as an unpublished Bell laboratories Technical Note (1957).Lowe,
David G., 2004. Distinctive image features from scale-invariant keypoints. Internat. J. Comput. Vision 60 (2), 91–110.
[84]Lu, Zhengdong, Leen, Todd K., 2007. Penalized probabilistic clustering. Neural
Comput. 19 (6), 1528–1567.
[85]Lukashin, A.V., Lukashev, M.E., Fuchs, R., 2003. Topology of gene expression
networks as revealed by data mining and modeling. Bioinformatics 19 (15), 1909–1916.
[86]MacQueen, J., 1967. Some methods for classification and analysis of multivariate
observations. In: Fifth Berkeley Symposium on Mathematics. Statistics and Probability. University of California Press, pp. 281–297.
[87]Mallows, C.L., 1957. Non-null ranking models. Biometricka 44, 114–130. [88]Mao, J., Jain, A.K., 1996. A self-organizing network for hyper-ellipsoidal
clustering (HEC). IEEE Trans. Neural Networks 7 (January), 16–29.
[89]McLachlan, G.L., Basford, K.E., 1987. Mixture Models: Inference and
Applications to Clustering. Marcel Dekker.
[90]Meila, Marina, 2003. Comparing clusterings by the variation of information. In:
COLT, pp. 173–187.
[91]Meila, Marina, 2006. The uniqueness of a good optimum for k-means. In: Proc.
23rd Internat. Conf. Machine Learning, pp. 625–632.
[92]Meila, Marina, Shi, Jianbo, 2001. A random walks view of spectral segmentation.
In: Proc. AISTATAS. [93]Merriam-Webster
Online
Dictionary,
2008.
Cluster
analysis.
[94]Mirkin, Boris, 1996. Mathematical Classification and Clustering. Kluwer
Academic Publishers.
[95]Moore, Andrew W., 1998. Very fast EM-based mixture model clustering using
multiresolution kd-trees. In: NIPS, pp. 543–549.
[96]Motzkin, T.S., Straus, E.G., 1965. Maxima for graphs and a new proof of a
theorem of Turan. Canadian J. Math. 17, 533–540.
[97]Muja, M., Lowe, D.G., 2009. Fast approximate nearest neighbors with automatic
algorithm configuration. In: Proc. Internat. Conf. on Computer Vision Theory and Applications (VISAPP’09).
[98]Newman, M.E.J., 2006. Modularity and community structure in networks. In:
Proc. National Academy of Sciences, USA.