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An Algorithm for Optimal Partitioning of Data on an Interval

Brad Jackson·Jeffrey D. Scargle·David Barnes·Sundararajan Arabhi·Alina Alt·Peter Gioumousis·Elyus Gwin·Paungkaew Sangtrakulcharoen·Linda Tan·Tun Tao Tsai
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Abstract

Many signal processing problems can be solved by maximizing the fitness of a segmented model over all possible partitions of the data interval. This letter describes a simple but powerful algorithm that searches the exponentially large space of partitions of $N$ data points in time $O(N^2)$. The algorithm is guaranteed to find the exact global optimum, automatically determines the model order (the number of segments), has a convenient real-time mode, can be extended to higher dimensional data spaces, and solves a surprising variety of problems in signal detection and characterization, density estimation, cluster analysis and classification.

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Brad Jackson, Jeffrey D. Scargle, David Barnes, Sundararajan Arabhi, Alina Alt, Peter Gioumousis, Elyus Gwin, Paungkaew Sangtrakulcharoen, Linda Tan, Tun Tao Tsai. 2003-09-17. An Algorithm for Optimal Partitioning of Data on an Interval. https://doi.org/10.1109/lsp.2001.838216

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