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Clustering of Trajectories using Non-Parametric Conformal DBSCAN Algorithm | IEEE Conference Publication | IEEE Xplore

Clustering of Trajectories using Non-Parametric Conformal DBSCAN Algorithm


Abstract:

Technology innovation has provided the opportunity to study the characteristics of natural human mobility. In this paper, we look at how to identify interesting clusters ...Show More

Abstract:

Technology innovation has provided the opportunity to study the characteristics of natural human mobility. In this paper, we look at how to identify interesting clusters (by different individuals or other naturally defined groups) in a family of trajectory traces. We focus on coarse-grained, sparsely sampled trajectories inferred from sporadic occurrences in an unsupervised setting. This is a challenging setting due to difficulties in selecting features and similarity measures, and due to lack of prior knowledge of data distribution. We propose a non-parametric clustering algorithm, which makes little assumptions on prior knowledge of both data distribution and cluster properties. Our algorithm, Conformal DBSCAN, combines density-based DBSCAN clustering with the statistical conformal prediction framework. We first identify groups of highly similar trajectories as the initial seeds of clusters, similar to DBSCAN. Then we include additional trajectories that belong to this cluster, with a guaranteed statistical confidence level, derived by an improved conformal prediction framework. This allows the clustering algorithm to automatically adapt to different data distributions. Our algorithms are shown to significantly outperform alternative clustering algorithms on several artificial and real-world datasets.
Date of Conference: 04-06 May 2022
Date Added to IEEE Xplore: 18 July 2022
ISBN Information:
Conference Location: Milano, Italy

References

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