2026-04-14T07:21:28Zhttps://recercat.cat/oai/request

oai:recercat.cat:10256/151242024-06-18T12:17:18Zcom_2072_452955com_2072_2054col_2072_453069

IBVis: Interactive Visual Analytics for Information Bottleneck Based Trajectory Clusterin Guo, Yuejun Xu, Qing Sbert, Mateu Entropia (Teoria de la informació) Entropy (Information theory) Analyzing trajectory data plays an important role in practical applications, and clustering is one of the most widely used techniques for this task. The clustering approach based on information bottleneck (IB) principle has shown its effectiveness for trajectory data, in which a predefined number of the clusters and an explicit distance measure between trajectories are not required. However, presenting directly the final results of IB clustering gives no clear idea of both trajectory data and clustering process. Visual analytics actually provides a powerful methodology to address this issue. In this paper, we present an interactive visual analytics prototype called IBVis to supply an expressive investigation of IB-based trajectory clustering. IBVis provides various views to graphically present the key components of IB and the current clustering results. Rich user interactions drive different views work together, so as to monitor and steer the clustering procedure and to refine the results. In this way, insights on how to make better use of IB for different featured trajectory data can be gained for users, leading to better analyzing and understanding trajectory data. The applicability of IBVis has been evidenced in usage scenarios. In addition, the conducted user study shows IBVis is well designed and helpful for users This work has been funded by Natural Science Foundation of China (61179067, 61771335) and Spanish ministry MINECO (TIN2016-75866-C3-3-R) 2024-06-18T12:17:18Z 2024-06-18T12:17:18Z 2024-06-18T12:17:18Z 2018-03-02 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion peer-reviewed http://hdl.handle.net/10256/15124 info:eu-repo/semantics/altIdentifier/doi/10.3390/e20030159 info:eu-repo/semantics/altIdentifier/eissn/1099-4300 MINECO/PE 2016-2019/TIN2016- 75866-C3-3-R Attribution 4.0 Spain http://creativecommons.org/licenses/by/4.0/es/ info:eu-repo/semantics/openAccess MDPI (Multidisciplinary Digital Publishing Institute) Entropy, 2018, vol. 20, núm. 3, p. 159 Articles publicats (D-IMA)