Mining representative movement patterns through compression

Phan Nhat Hai, Dino Ienco, Pascal Poncelet, Maguelonne Teisseire

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

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 different 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 combining 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.

Original languageEnglish (US)
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 17th Pacific-Asia Conference, PAKDD 2013, Proceedings
Pages314-326
Number of pages13
EditionPART 1
DOIs
StatePublished - 2013
Externally publishedYes
Event17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013 - Gold Coast, QLD, Australia
Duration: Apr 14 2013Apr 17 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume7818 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013
Country/TerritoryAustralia
CityGold Coast, QLD
Period4/14/134/17/13

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Keywords

  • Compressibility
  • MDL
  • Moving objects
  • Spatio-temporal data
  • Top-k

Fingerprint

Dive into the research topics of 'Mining representative movement patterns through compression'. Together they form a unique fingerprint.

Cite this