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 -Cluster prototypes

Clustering Experiences  Obtaining data (experiences)  Dynamic Time Warping (DTW)  Distance matrix  Cluster prototypes

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

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

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

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

DTW (above) vs. DDTW (below)

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