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The problem of change-point estimation is considered under a generalframework where the data are generated by unknown stationary ergodic processdistributions. In this context, the consistent estimation of the number ofchange-points is provably impossible. However, it is shown that a consistentclustering method may be used to estimate the number of change points, underthe additional constraint that the correct number of process distributions thatgenerate the data is provided. This additional parameter has a naturalinterpretation in many real-world applications. An algorithm is proposed thatestimates the number of change-points and locates the changes. The proposedalgorithm is shown to be asymptotically consistent; its empirical evaluationsare provided.

A consistent clustering-based approach to estimating the number of change-points in highly dependent time-series