All in One: Mining Multiple Movement Patterns

Nhathai Phan (), Pascal Poncelet and Maguelonne Teisseire
Additional contact information
Nhathai Phan: Information Systems Department, New Jersey Institute of Technology, University Heights, Newark, NJ 07102-1982, USA
Pascal Poncelet: Lirmm Laboratory, University Montpellier 2, 161 Rue Ada 34095, Montpellier, Cedex 5, France
Maguelonne Teisseire: Tetis Laboratory, Irstea Montpellier, 500 Rue Jean-Francois Breton 34093, Montpellier, Cedex 5, France

International Journal of Information Technology & Decision Making (IJITDM), 2016, vol. 15, issue 05, 1115-1156

Abstract: Recent improvements in positioning technology have led to a much wider availability of massive moving object data. A crucial task is to find the moving objects that travel together. In common, these object sets are called object movement patterns. Due to the emergence of many different kinds of object movement patterns in recent years, different approaches have been proposed to extract them. However, each approach only focuses on mining a specific kind of patterns. It is costly and time consuming to mine and manage various number of patterns, since we have to execute a large number of different pattern mining algorithms. Moreover, we have to execute these algorithms again whenever new data are added to the existing database. To address these issues, we first redefine movement patterns in the itemset context. Second, we propose a unifying approach, named GeT_Move, which uses a frequent closed itemset-based object movement pattern-mining algorithm to mine and manage different patterns. GeT_Move is developed in two versions which are GeT_Move and Incremental GeT_Move. To optimize the efficiency and to free the parameters setting, we further propose a Parameter Free Incremental GeT_Move algorithm. Comprehensive experiments are performed on real and large synthetic datasets to demonstrate the effectiveness and efficiency of our approaches.

Keywords: Object movement pattern; frequent closed itemset; unifying approach; trajectories (search for similar items in EconPapers)
Date: 2016
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0219622016500280
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:ijitdm:v:15:y:2016:i:05:n:s0219622016500280

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0219622016500280

Access Statistics for this article

International Journal of Information Technology & Decision Making (IJITDM) is currently edited by Yong Shi

More articles in International Journal of Information Technology & Decision Making (IJITDM) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().