My Clustering Bibliography

4 12 2009

Agusta, Y. (2004). Minimum Message Length Mixture Modelling for Uncorrelated and Correlated Continuous Data Applied to Mutual Funds Classification, Ph.D. Thesis, School of Computer Science and Software Engineering, Monash University, Clayton, 3800 Australia

Agusta, Y. and Dowe, D.L. (2002a). MML Clustering of Continuous-Valued Data using Gaussian and t Distributions, in B. McKay and J. Slaney (eds), Lecture Notes in Artificial Intelligence 2557, Proceedings of the 15th Australian Joint Conference on Artificial Intelligence (AI02), Springer-Verlag, Berlin, Germany, pp. 143-154

Agusta, Y and Dowe, D.L. (2002b). Clustering of Gaussian and t Distribution using Minimum Message Length, in M. Sasikumar, H. J. Jayprasad and M. Kavitha (eds), Artificial Intelligence: Theory and Practice, Proceedings of the International Conference Knowledge-Based Computer Systems (KBCS-2002), Vikas Publishing House Pvt. LTD., New Delhi, India, pp. 289-299.

Agusta, Y and Dowe, D.L. (2003). Unsupervised Learning of Correlated Multivariate Gaussian Mixture Models using MML, in T.D. Gideon and L.C. Fung (eds), Lecture Notes in Artificial Intelligence 2903, Proceedings of the 16th Australian Joint Conference on Artificial Intelligence (AI03), Springer-Verlag, Berlin, Germany, pp. 477-489.

Akaike, H. (1974). A New Look at the Statistical Model Identification, IEEE Transaction on Automatic Control AC-19(6): 716-723.

Bezdek, J. C. (1981). Pattern Recognition with Fuzzy Objective Function Algoritmss, Plenum Press, New York.

Cheeseman, P. and Stutz, J. (1996). Bayesian Classification (AutoClass): Theory and Results, in U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth and R. Uthurusamy (eds), Advances in Knowledge Discovery and Data Mining, AAAI Press/MIT Press, Cambridge, MA, pp. 153-180.

Figueiredo, M. A. and Jain A.K. (2002). Unsupervised Learning of Finite Mixture Models. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(3): 381-396.

Fraley, C. and Raftery, A. E. (1998). MCLUST: Software for Model-Based Cluster and Discriminant Analysis, Technical Report 342, Department of Statistics, University of Washington, Box 354322, Seattle, WA, USA.

MacQueen, J. B. (1967). Some Methods for classification and Analysis of Multivariate Observations, Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, University of California Press, 1: 281-297.

McLachlan, G.J. and Peel, D. (2002a). On Computational Aspects of Clustering via Mixtures of Normal and t-Components, Proceedings of the American Statistical Association (Bayesian Statistical Science Section), Indianapolis, Alexandria, Virginia.

McLachlan, G.J. and Peel, D. (2002b). Finite Mixture Models, John Wiley and Sons, New York.

McLachlan, G. J., Peel, D., Basford, K. E. and Adams, P. (1999). The EMMIX Software for the Fitting of Mixtures of Normal and t-Components, Journal of Statistical Software 4(2): 1087-1092.

Miyamoto, S. and Agusta, Y. (1995). An Efficient Algorithm for L1 Fuzzy c-Means and its Termination, Control and Cybernetics 24(4): 422-436.

Miyamoto, S. and Agusta, Y. (1995). Algorithms for L1 and Lp Fuzzy C-Means and Their Convergence, in C. Hayashi, N. Oshumi, K. Yajima, Y. Tanaka, H. H. Bock and Y. Baba (eds), Data Science, Classification, and Related Methods, Springer-Verlag, Tokyo, Japan, pp. 295-302.

Miyamoto S. and Nakayama, Y. (2003). Algorithms of Hard C-Means Clustering Using Kernel Functions in Support Vector Machines, Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 7, No. 1, pp. 19–24.

Miyamoto, S. and Suizu, D. (2003). Fuzzy C-Means Clustering Using Kernel Functions in Support Vector Machines, Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 7, No. 1, pp. 25–30.

Neal, R. M. (1991). Bayesian Mixture Modeling by Monte Carlo Simulation, Technical Report CRG-TR-91-2, Department of Computer Science, University of Toronto, Toronto, Canada.

Pena, J. M., Lozano, J. A. and Larranaga, P. (1999). An empirical comparison of four initialization methods for the k-means algorithm. Pattern Recognition Lett., 20:1027-1040.

Schwarz, G. (1978). Estimating the Dimension of a Model, The Annals of Statistics 6: 461 – 464.

Shannon, C. E. (1948). A Mathematical Theori of Communication, Bell System Tech. Journal 27: 379-423.

Tibshirani, R., Walter, G. and Hastie, T. (2000). Estimating the Number of Clusters in a Dataset using the Gap Statistics, Technical Report 208, Department of Statistics, Stanford University, Standford, CA 94305, USA.

Wallace, C. S. (1986). An Improved Program for Classification, Proceedings of the 9th Australian Computer Science Conference (ACSC-9), Vol. 8, Monash University, Australia, pp. 357-366.

Wallace, C. S. and Boulton, D. M. (1968). An Information Measure for Classification, Computer Journal 11(2): 185-194.

Wallace, C. S. and Dowe D. L. (1994). Intrinsic Classification by MML – the Snob Program, Proceedings of the 7th Autralian Joint Conference on Artificial Intelligence (AI94), World Scientific, Singapore, pp. 37-44.

Wallace, C. S. and Dowe, D. L. (1997). MML Mixture Modelling of Multi-State, Poisson, von Mises Circular and Gaussian Distribusions, Proceedings of the 6th International Workshop on Artificial Intelligence and Statistics, Fort Launderdale, Florida, pp. 529-536.

Wallace, C. S. and Dowe, D. L. (2000). MML Clustering of Multi-state, Poisson, von Mises Circular and Gaussian Distributions, Statistics and Computing 10: 73-83.

Wallace, C. S. and Freeman, P. R. (1987). Estimation and Inference by Compact Coding, Journal of the Royal Statistical Society B 49(3): 240-265.

Related Theory

Bernardo J.M. and Smith A.F.M. (1994). Bayesian Theory, Wiley, Chichester, UK.

Dowe D.L., Oliver J.J. and Wallace C.S. (1996). MML Estimation of the Parameters of the Spherical Fisher Distribution, in S. Arikawa and A.K. Sharma (eds), Lecture Notes in Artificial Intelligence 1160, Proceedings of the 7th International Workshop on Algorithmic Learning Theory, (ALT’96), Springer-Verlag, Heidelberg, Germany, pp. 213-227.

Fitzgibbon L.J., Dowe D.L. and Allison L. (2002a). Univariate Polynomial Inference by Monte Carlo Message Length Approximation, in C. Sammut and A. Hoffmann (eds), Proceedings of 19th International Conference on Machine Learning (ICML-2002), Morgan Kaufman, Sydney, pp. 147-154.

Fitzgibbon L.J., Dowe D.L. and Allison L. (2002b). Change-Point Estimatin Using New Minimum Message Length Approximations, in M. Ishizuka and A. Sattar (eds), Lecture Notes in Artificial Intelligence 2417, Seventh Pacific Rim International Conference on Artificial Intelligence (PRICAI-2002), Springer-Verlag, Berlin, Germany, pp. 244-254.

Girolami, M. (2002). Mercel Kernel Based Clustering in Feature Space, IEEE Transactions on Neural Networks, Vol. 13, No. 3, pp. 761-766.

Lam E. (2000). Improved Approximation in MML, Honours’ Thesis, School of Computer Science and Software Engineering, Monash University, Clayton, Victoria 3800, Australia.

Lindley, D.V. (1972). Bayesian Statistics, A Review, SIAM, Philadelpia, PA, USA.

Wallace, C. S. and Dowe, D. L. (1999a). Minimum Message Length and Kolmogorov Complexity, Computer Journal 42(4): 270-283. Special issue on Kolmogorov Complexity.

Wallace, C. S. and Dowe, D. L. (1999b). Refinements of MDL and MML Coding, Computer Journal 42(4): 345-347. Special issue on Kolmogorov Complexity.


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4 responses

10 06 2010
cawas

bagaimanakah cara menentukan jumlah cluster dalam clustering untuk mendapatkan clustering yang optimal?

16 06 2010
Yudi Agusta

Ada berbagai macam cara, bisa dilihat di tulisan saya di sini: https://yudiagusta.wordpress.com/k-means/ atau di sini: https://yudiagusta.wordpress.com/mixture-modelling/ (tapi belum lengkap :().

Semoga menjawab.

24 12 2009
solih

Salam kenal pa Yudi….saya juga sangat tertarik riset tentang mixture model, beberapa kajian saya berkaitan dengan model distribusi data masa hidup (life time) Weibull khususnya dan beberapa model distribusi peluang keluarga eksponensial lainnya….ada sedikit kesulitan dalam pengujian kecocokan model dari hasil estimasi parameter yang telah diperoleh untuk mixture model. Mungkin ada pengalaman yang telah dilakukan pa Yudi dalam membuat kecocokan model mixture tsb? Trims (Solih)

28 12 2009
Yudi Agusta

Kecocokan yang dimaksud kecocokan dengan model mana? Dalam artian, model mana yang akan dijadikan referensi untuk menjadi dasar pencocokan. Metode mixture model khan termasuk metode unsupervised classification, yang umumnya hanya menghasilkan model dari data, tanpa ada model referensi atau label referensi yang menjadi pemandunya.

Demikian.

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