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Open AccessFeature PaperArticle

IBVis: Interactive Visual Analytics for Information Bottleneck Based Trajectory Clustering

1
School of Computer Science and Technology, Tianjin University, Tianjin 300350, China
2
Department of Informàtica i Matemàtica Aplicada, University of Girona, 17071 Girona, Spain
*
Authors to whom correspondence should be addressed.
Entropy 2018, 20(3), 159; https://doi.org/10.3390/e20030159
Received: 11 January 2018 / Revised: 27 February 2018 / Accepted: 28 February 2018 / Published: 2 March 2018
(This article belongs to the Special Issue Information Theory Application in Visualization)
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. View Full-Text
Keywords: visual analytics; information bottleneck; trajectory clustering visual analytics; information bottleneck; trajectory clustering
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MDPI and ACS Style

Guo, Y.; Xu, Q.; Sbert, M. IBVis: Interactive Visual Analytics for Information Bottleneck Based Trajectory Clustering. Entropy 2018, 20, 159.

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