NDLI: TODMIS: mining communities from trajectories

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TODMIS: mining communities from trajectories

Content Provider	ACM Digital Library
Author	Liu, Siyuan Wang, Shuhui Misra, Archan Krishnan, Ramayya Jayarajah, Kasthuri
Abstract	Existing algorithms for trajectory-based clustering usually rely on simplex representation and a single proximity-related distance (or similarity) measure. Consequently, additional information markers (e.g., social interactions or the semantics of the spatial layout) are usually ignored, leading to the inability to fully discover the communities in the trajectory database. This is especially true for human-generated trajectories, where additional fine-grained markers (e.g., movement velocity at certain locations, or the sequence of semantic spaces visited) can help capture latent relationships between cluster members. To address this limitation, we propose TODMIS: a general framework for Trajectory cOmmunity Discovery using Multiple Information Sources. TODMIS combines additional information with raw trajectory data and creates multiple similarity metrics. In our proposed approach, we first develop a novel approach for computing semantic level similarity by constructing a Markov Random Walk model from the semantically-labeled trajectory data, and then measuring similarity at the distribution level. In addition, we also extract and compute pair-wise similarity measures related to three additional markers, namely trajectory level spatial alignment (proximity), temporal patterns and multi-scale velocity statistics. Finally, after creating a single similarity metric from the weighted combination of these multiple measures, we apply dense sub-graph detection to discover the set of distinct communities. We evaluated TODMIS extensively using traces of (i) student movement data in a campus, (ii) customer trajectories in a shopping mall, and (iii) city-scale taxi movement data. Experimental results demonstrate that TODMIS correctly and efficiently discovers the real grouping behaviors in these diverse settings.
Starting Page	2109
Ending Page	2118
Page Count	10
File Format	PDF
ISBN	9781450322638
DOI	10.1145/2505515.2505552
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-10-27
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Semantic information Trajectory community discovery Multiple information
Content Type	Text
Resource Type	Article

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