Abstract
The segmentation problem arises in many applications in data mining, A.I. and statistics, including segmenting time series, decision tree algorithms and image processing. In this paper, we consider a range of criteria which may be applied to determine if some data should be segmented into two or regions. We develop a information theoretic criterion (MML) for the segmentation of univariate data with Gaussian errors. We perform simulations comparing segmentation methods (MML, AIC, MDL and BIC) and conclude that the MML criterion is the preferred criterion. We then apply the segmentation method to financial time series data.
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References
H. Akaike. Information theory and an extension of the maximum likelihood principle. In B.N. Petrov and F. Csaki, editors, Proc. of the 2nd International Symposium on Information Theory, pages 267–281, 1973.
R.A. Baxter and J.J. Oliver. The kindest cut: minimum message length segmentation. In S. Arikawa and A. Sharma, editors, Lecture Notes in Artificial Intelligence 1160, Algorithmic Learning Theory, ALT-96, pages 83–90, 1996.
J.H. Conway and N.J.A Sloane. Sphere Packings, Lattices and Groups. Springer-Verlag, London, 1988.
B. Dom. MDL estimation with Small Sample Sizes including an application to the problem of segmenting binary strings using bernoulli models. Technical Report RJ 9997 (89085) 12/15/95, IBM Research Division, Almaden Research Center, 650 Harry Rd, San Jose, CA, 95120–6099, 1995.
G. Koop and S.M. Potter. Bayes Factors and nonlinearity: Evidence from economic time series. UCLA Working Paper, August 1995, submitted to Journal of Econometrics.
Mengxiang Li. Minimum description length based 2-D shape description. In IEEE 4th Int. Conf. on Computer Vision, pages 512–517, May 1992.
Z. Liang, R.J. Jaszczak, and R.E. Coleman. Parameter estimation of finite mixtures using the EM algorithm and information criteria with applications to medical image processing. IEEE Trans. on Nuclear Science, 39(4):1126–1133, 1992.
J.J. Oliver and D.J. Hand. Introduction to minimum encoding inference. Technical report TR 4-94, Dept. of Statistics, Open University, Walton Hall, Milton Keynes, MK7 6AA, UK, 1994. Available on the WWW from http://www.cs.monash.edu.au/~jono.
J.J. Oliver, Baxter R.A., and Wallace C.S. Unsupervised Learning using MML. In Machine Learning: Proc. of the Thirteenth International Conference (ICML 96), pages 364–372. Morgan Kaufmann Publishers, San Francisco, CA, 1996. Available on the WWW from http://www.cs.monash.edu.au/~jono.
B. Pfahringer. Compression-based discretization of continuous attributes. In Machine Learning: Proc. of the Twelfth International Workshop, pages 456–463, 1995.
J.R. Quinlan. Improved use of continuous attributes in C4.5. Journal of Artificial Intelligence, 4:77–90, 1996.
J. Rissanen. Modeling by shortest data description. Automatica, 14:465–471, 1978.
G. Schwarz. Estimating dimension of a model. Ann. Stat., 6:461–464, 1978.
S.L. Sclove. On segmentation of time series. In S. Karlin, T. Amemiya, and L. Goodman, editors, Studies in econometrics, time series, and multivariate statistics, pages 311–330. Academic Press, 1983.
C.W. Therrien. Decision, estimation, and classification: an introduction to pattern recognition and related topics. Wiley, New York, 1989.
H. Tong. Non-linear time series: a dynamical system approach. Clarendon Press, Oxford, 1990.
C.S. Wallace and D.M. Boulton. An information measure for classification. Computer Journal, 11:185–194, 1968.
C.S. Wallace and P.R. Freeman. Estimation and inference by compact coding. Journal of the Royal Statistical Society (Series B), 49:240–252, 1987.
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© 1998 Springer-Verlag Berlin Heidelberg
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Oliver, J.J., Baxter, R.A., Wallace, C.S. (1998). Minimum message length segmentation. In: Wu, X., Kotagiri, R., Korb, K.B. (eds) Research and Development in Knowledge Discovery and Data Mining. PAKDD 1998. Lecture Notes in Computer Science, vol 1394. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-64383-4_19
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DOI: https://doi.org/10.1007/3-540-64383-4_19
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