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Evaluation of Trajectory Pattern Mining Algorithms: Addressing Limitations and Enhancing Performance

This project report evaluates state-of-the-art trajectory pattern mining algorithms, exploring their limitations and proposing enhancements to improve their performance. The report focuses on algorithms such as TRACLUS, TRACLUS Ulm, and DivCluST, highlighting their strengths and weaknesses in extracting meaningful patterns from trajectory data.

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Evaluation of Trajectory Pattern Mining Algorithms: Addressing Limitations and Enhancing Performance

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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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