Unveiling Birch’s Dominance In Trajectory Clustering: A Comparative Analysis
Author(s):
V S. Praven Kumar, Sajimon Abraham, Sijo Thomas , Nishad.A, Benymol Jose
Keywords:
Moving object trajectory, Point of Interest, Spatio-temporal data
Abstract
The movement of an object and its associated data is of paramount importance for prompt interventions in challenging areas related to human mobility and the trajectories of moving objects. Spatio-temporal data constitutes the primary resource for developing applications in mobility-based management across all aspects of human existence and other objects. Mobility can be tracked when latitude, longitude, and time information are available. The inferences drawn from mobility data can be utilized for various purposes, particularly in applications where the distinctive features of moving objects hold significance. Mobility data finds utility in diverse studies and predictive applications, such as users' travel experiences, geomatic applications, and transportation system analysis. The importance of analyzing human mobility data spans from epidemic modeling to traffic prediction. There is a need for quantitative models that can encompass the statistical characteristics of individual human trajectories, urban planning, traffic monitoring, and location-based services, and to predict the spread of pandemic diseases. Incorporating Points of Interest in semantic regions allows for the augmentation of attributes in trajectory data, resulting in attribute-enriched trajectories. The SemTraClus algorithm [6] is employed for identifying and clustering semantic regions in spatio-temporal trajectories. This study entails a comparison of the performance of DBSCAN clustering in SemTraClus with other clustering methods, namely K-means, and BRICH. The evaluation of its performance and accuracy considers user participation weighting and the Silhouette score, all using the same dataset. The comparative study of clustering methods is conducted using a real trajectory dataset from the Geo-life project of Microsoft Research Asia [18].
Article Details
Unique Paper ID: 167220

Publication Volume & Issue: Volume 11, Issue 3

Page(s): 636 - 648
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