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Project Report

Project Report. Steve Mussmann and Alla Petrakova. Evaluating Baselines. Exploring limitations of state-of-the-art algorithms. Gianotti.

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Project Report

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  1. Project Report Steve Mussmann and Alla Petrakova

  2. Evaluating Baselines • Exploring limitations of state-of-the-art algorithms

  3. Gianotti • F.Giannotti,M.Nanni,F.Pinelli,andD.Pedreschi,“Trajectory pattern mining,” in Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2007, pp. 330–339. • An algorithm that seeks to find aggregate motion behaviors from trajectories. • Behaviors are defined as a sequence of rectangular regions • Gives very redundant results • Requiring regions to be rectangular restricts the shape of extracted patterns

  4. Gianotti

  5. TRACLUS • J. gil Lee and J. Han, “Trajectory clustering: A partition-and- group framework,” in In SIGMOD, 2007, pp. 593–604. • The goal of TRACLUS is to detect similar portions of trajectories, • Not capable of simultaneously extracting both dense and sparse trajectory clusters • Parameters are set globally • Modifying its parameters such that it find sparser clusters leads to redundant clusters in denser regions

  6. TRACLUS

  7. Ulm • M. Ulm and N. Brandie, “Robust online trajectory clustering without computing trajectory distances,” in Pattern Recognition (ICPR), 2012 21st International Conference on. IEEE, 2012, pp. 2270–2273. • Algorithm that seeks to find clusters in the form of vector fields defined on a connected spatial set • Performs well on datasets that are well structured such as Vehicle Motion Trajectory Dataset • Not well-suited for unstructured datasets (e.g., Greek Trucks) • Behaves much like the algorithms that cluster trajectories as a whole

  8. Ulm

  9. DivCluST • H. ru Wu, M.-Y. Yeh, and M.-S. Chen, “Profiling moving ob- jects by dividing and clustering trajectories spatiotemporally,” IEEE Transactions on Knowledge and Data Engineering, vol. 99, no. PrePrints, 2012. • Algorithm that seeks to find regional typical moving styles in the form of mean lines • Has trouble when there is large variation in the trajectory density • In the high density regions, the mean lines are very cluttered and overlapping • Because the model is restricted to straight mean lines rather than curves, motion that would be better described as a curve is instead required to be described as one long mean line or a sequence of short mean lines

  10. DivCluST

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