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A Method for Clustering the Experiences of a Mobile Robot that Accords with Human Judgments

A Method for Clustering the Experiences of a Mobile Robot that Accords with Human Judgments. Tim Oates, Matthew D. Schmill, Paul R. Cohen Discussant: Jacek Rawicki. Introduction. A robot agent Unsupervised method of learning Individual time series Clustering: Measure of similarity

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A Method for Clustering the Experiences of a Mobile Robot that Accords with Human Judgments

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  1. A Method for Clustering the Experiences of a Mobile Robot that Accords with Human Judgments Tim Oates, Matthew D. Schmill, Paul R. Cohen Discussant: Jacek Rawicki

  2. Introduction • A robot agent • Unsupervised method of learning • Individual time series • Clustering: • Measure of similarity • Cluster prototypes

  3. Clustering Experiences • Obtaining data (experiences) • Dynamic Time Warping (DTW) • Distance matrix • Cluster prototypes

  4. Evaluation • High levels of accordance, but: • Ordering effect (greedy clustering algorithm) • Errors from DTW (manipulation of time dimension)

  5. Conclusion • High level of accordance • A simple optimization technique My ( humble : – ) suggestions: • Use DDTW instead of DTW • Use other clustering techniques

  6. Errors in DTW • a pathological result: the algorithm tries to explain variability in the Y-axis by warping the X-axis.

  7. Improvement of DTW - DDTW • Derivative Dynamic Time Warping takes into consideration the first derivative.

  8. DTW (above) vs. DDTW (below)

  9. References • DDTW:Keogh E. J., Pazzani M. J.: Derivative Dynamic Time Warping, • Other clustering techniques: • e.g. Jain A.K., Murty M. N., Flynn P. J. Data Clustering: A Review

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