International Journal of Soft Computing

Year: 2014
Volume: 9
Issue: 3
Page No. 131 - 137

An Initialization Method for K-Means Algorithm Using Binary Search Technique

Authors : Yugal Kumar and G. Sahoo

References

Bradley, P.S. and U.M. Fayyad, 1998. Refining initial points for K-means clustering. Proceedings of the 15th International Conference on Machine Learning, July 24-27, 1998, Morgan Kaufmann, San Francisco, pp: 91-99.

Bradley, P.S., O.L. Mangasarian and W.N. Street, 1997. Clustering Via Concave Minimization. In: Advances in Neural Information Processing Systems, Mozer, M.C., M.I. Jordan and T. Petsche (Eds.). MIT Press, Cambridge, MA, USA., pp: 368-374.

Cao, F., J. Liang and G. Jiang, 2009. An initialization method for the K-Means algorithm using neighborhood model. Comput. Math. Appl., 58: 474-483.
CrossRef  |  Direct Link  |  

Chan, Z.S.H., L. Collins and N. Kasabov, 2006. An efficient greedy K-Means algorithm for global gene trajectory clustering. Expert Syst. Appl., 30: 137-141.
CrossRef  |  Direct Link  |  

De Amorim, R.C. and B. Mirkin, 2012. Minkowski metric, feature weighting and anomalous cluster initializing in K-Means clustering. Pattern Recognit., 45: 1061-1075.
CrossRef  |  Direct Link  |  

De Amorim, R.C. and P. Komisarczuk, 2012. On initializations for the minkowski weighted K-means. Proceedings of the 11th International Conference on Advances in Intelligent Data Analysis XI, October 25-27, 2012, Helsinki, Finland, pp: 45-55.

Dubes, R. and A.K. Jain, 1979. Validity studies in clustering methodologies. Pattern Recognit., 11: 235-254.
CrossRef  |  Direct Link  |  

Duda, R. and P. Hart, 1973. Pattern Classification and Scene Analysis. John Wiley and Sons, New York.

Dudoit, S. and J. Fridlyand, 2002. A prediction-based resampling method for estimating the number of clusters in a dataset. Genome Biol., Vol. 3. 10.1186/gb-2002-3-7-research0036

Everitt, B.S., 1979. Unresolved problems in cluster analysis. Biometrics, 35: 169-181.
Direct Link  |  

Feng, Y. and G. Hamerly, 2007. PG-Means: Learning the Number of Clusters in Data. In: Advances in Neural Information Processing Systems, Scholkopf, B., J.C. Platt and T. Hofmann (Eds.). MIT Press, Cambridge, MA., USA., ISBN-13: 9780262195683, pp: 393-400.

Hartigan, J.A., 1975. Algorithm CHAID. Clustering Algorithms. John Wiley and Sons, New York.

Hatamlou, A., 2012. In search of optimal centroids on data clustering using a binary search algorithm. Pattern Recognit. Lett., 33: 1756-1760.
CrossRef  |  Direct Link  |  

Huang, J.Z., J. Xu, M. Ng and Y. Ye, 2008. Weighting Method for Feature Selection in K-Means. In: Computational Methods of Feature Selection, Liu, H. and H. Motoda (Eds.). Chapman and Hall, New York, pp: 193-209.

Hubert, L.J. and J.R. Levin, 1976. A general statistical framework for assessing categorical clustering in free recall. Psychol. Bull., 83: 1072-1080.
CrossRef  |  Direct Link  |  

Ishioka, T., 2005. An expansion of X-means for automatically determining the optimal number of clusters. Proceedings of the International Conference on Computational Intelligence, July 4-6, 2005, Calgary, AB., Canada, pp: 91-96.

Jain, A.K., 2010. Data clustering: 50 years beyond K-means. Pattern Recogn. Lett., 31: 651-666.
CrossRef  |  Direct Link  |  

Jain, A.K., M.N. Murty and P.J. Flynn, 1999. Data clustering: A review. ACM Comput. Surv., 31: 264-323.
CrossRef  |  Direct Link  |  

Kao, Y.T., E. Zahara and I.W. Kao, 2008. A hybridized approach to data clustering. Expert Syst. Applic., 34: 1754-1762.
CrossRef  |  Direct Link  |  

Krzanowski, W.J. and Y.T. Lai, 1988. A criterion for determining the number of groups in a data set using sum-of-squares clustering. Biometrics, 44: 23-34.
Direct Link  |  

Kuncheva, L.I. and D.P. Vetrov, 2006. Evaluation of stability of k-means cluster ensembles with respect to random initialization. IEEE Trans. Pattern Anal. Mach. Intell., 28: 1798-1808.
CrossRef  |  

Likas, A., N. Vlassis and J.J. Verbeek, 2003. The global k-means clustering algorithm. Pattern Recognit., 36: 451-461.
CrossRef  |  

Lu, J.F., J.B. Tang, Z.M. Tang and J.Y. Yang, 2008. Hierarchical initialization approach for K-Means clustering. Pattern Recogn. Lett., 29: 787-795.
CrossRef  |  

MacQueen, J., 1967. Some methods for classification and analysis of multivariate observations. Proc. 5th Berkeley Symp. Math. Statist. Prob., 1: 281-297.
Direct Link  |  

Meila, M. and D. Heckerman, 1998. An experimental comparison of several clustering and initialization methods. Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, July 24-26, 1998, San Francisco, CA., pp: 386-395.

Milligan, G.W. and M.C. Cooper, 1985. An examination of procedures for determining the number of clusters in a data set. Psychometrika, 50: 159-179.
CrossRef  |  Direct Link  |  

Milligan, G.W., 1981. A discussion of procedures for determining the number of clusters in a data set. Proceedings of the Classification Society Meeting, May 31-June 2, 1981, Toront, Canada -.

Minaei-Bidgoli, B., A. Topchy and W.F. Punch, 2004. A comparison of resampling methods for clustering ensembles. Proceedings of the International Conference on Machine Learning: Models, Technologies and Application, June 21-24, 2004, Las Vegas, USA., pp: 939-945.

Mirkin, B., 2005. Clustering for Data Mining: A Data Recovery Approach. Chapman and Hall, London.

Modha, D.S. and W.S. Spangler, 2000. Clustering hyper text with applications to web searching. Proceedings of the 11th ACM on Hypertext and Hypermedia, May 30-June 3, 2000, San Antonio, TX., pp: 143-152.

Modha, D.S. and W.S. Spangler, 2003. Feature weighting in k-means clustering. Mach. Learn., 52: 217-237.
CrossRef  |  Direct Link  |  

Monti, S., P. Tamayo, J. Mesirov and T. Golub, 2003. Consensus clustering: A resampling-based method for class discovery and visualization of gene expression microarray data. Machine Learn., 52: 91-118.
Direct Link  |  

Mufti, G.B., P. Bertrand and L.E. Moubarki, 2005. Determining the number of groups from measures of cluster stability. Proceedings of the International Symposium on Applied Stochastic Models and Data Analysis, May 17-20, 2005, Brest, France, pp: 405-413.

Perruchet, C., 1983. Les epreuves de classifiabilite en analyses des donnees [Statistical tests of classifiability]. Tech. Rep. NT/PAA/ATR/MTI/810). C.N.E.T. Issy-Les-Moulineaux, France.

Selim, S.Z. and M.A. Ismail, 1984. K-means-type algorithms: A generalized convergence theorem and characterization of local optimality. IEEE Trans. Pattern Anal. Mach. Intell., 6: 81-87.
CrossRef  |  

Shen, J., S.I. Chang, E.S. Lee, Y. Deng and S.J. Brown, 2005. Determination of cluster number in clustering microarray data. Applied Math. Comput., 169: 1172-1185.
CrossRef  |  Direct Link  |  

Sneath, P.H.A. and R.R. Sokal, 1973. Numerical Taxonomy. W.H. Freeman and Company, San Francisco, USA., ISBN: 0-7167-0697-0.

Sugar, C.A., and G.M. James, 2003. Finding the number of clusters in a dataset: An information-theoretic approach. J. Am. Stat. Assoc., 98: 750-763.
Direct Link  |  

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