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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© 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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