An Algorithm for Optimal Partitioning of Data on an Interval

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.

3 pages, 1 figure, submitted to IEEE Signal Processing Letters, revised version with added references