Mining Representative Movement Patterns through Compression

Phan Nhat Hai 1, 2 Dino Ienco 2, 1 Pascal Poncelet 1 Maguelonne Teisseire 1, 2
1 ADVANSE - ADVanced Analytics for data SciencE
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
2 UMR TETIS - Territoires, Environnement, Télédétection et Information Spatiale
Abstract : Mining trajectories (or moving object patterns) from spatio-temporal data is an active research field. Most of the researches are devoted to extract trajectories that differ in their structure and characteristic in order to capture dif- ferent object behaviors. The first issue is constituted from the fact that all these methods extract thousand of patterns resulting in a huge amount of redundant knowledge that poses limit in their usefulness. The second issue is supplied from the nature of spatio-temporal database from which different types of patterns could be extracted. This means that using only a single type of patterns is not sufficient to supply an insightful picture of the whole database. Motivating by these issues, we develop a Minimum Description Length (MDL)-based approach that is able to compress spatio-temporal data combin- ing different kinds of moving object patterns. The proposed method results in a rank of the patterns involved in the summarization of the dataset. In order to validate the quality of our approach, we conduct an empirical study on real data to compare the proposed algorithms in terms of effectiveness, running time and compressibility.
Keywords : MDL Moving objects Spatio-temporal data Top-k Compressibility
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LIRMM | MIPS | AGROPOLIS | CIRAD | ADVANSE | IRSTEA | CNRS | AGROPARISTECH | UNIV-MONTPELLIER

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Phan Nhat Hai, Dino Ienco, Pascal Poncelet, Maguelonne Teisseire. Mining Representative Movement Patterns through Compression. PAKDD: Pacific-Asia Conference on Knowledge Discovery and Data Mining, Apr 2013, Gold Coast, Australia. pp.314-326, ⟨10.1007/978-3-642-37453-1_26⟩. ⟨lirmm-00798072⟩

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