How similarty measures effect on clustering result on life or learning trajectory data?
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Different similarity measures have been analyzed in the context of trajectory clustering. The impact of these measures on clustering results has been studied using various algorithms such as K-Means, DBSCAN, and hierarchical clustering. It has been found that the choice of similarity measure can significantly affect the clustering outcome. For example, in the analysis of trajectory data, the Haversine distance has been shown to be more efficient than Euclidean and Hausdorff distances in terms of spatial trajectory data . Similarly, the comparative study of similarity measures for trajectory clustering using the DBSCAN algorithm has shown that DTW and Frechet distance lead to the best clustering results, while LCSS and Hausdorff Cosine should be avoided . These findings highlight the importance of selecting an appropriate similarity measure for accurate trajectory clustering.
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| Papers (5) | Insight |
|---|---|
| The paper does not specifically mention the effect of similarity measures on clustering results for life or learning trajectory data. | |
05 Mar 2021 4 Citations | The paper focuses on ship trajectory clustering, not life or learning trajectory data. |
| The paper explores the impact of different similarity measures on trajectory clustering using a commercial flight dataset. | |
16 Citations | The paper proposes using similarity measures based on edge and vertex similarity to cluster trajectories, but does not specifically mention life or learning trajectory data. |
01 Feb 2021 4 Citations | The paper analyzes the effect of different similarity measures (Euclidean, Hausdorff, and Haversine distances) on clustering algorithms for trajectory data. |
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