Créer des clusters de trajectoires d'acheteurs par les structures de réseau

Translated title of the contribution: Clustering customer shopping trips with network structure

Ping Yan, Dajun Zeng

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

1 Citation (Scopus)

Abstract

Moving objects can be tracked with sensors such as RFID tags or GPS devices. Their movement can be represented as sequences of time-stamped locations. Studying such spatio-temporal movement sequences to discover spatial sequential patterns holds promises in many real-world settings. A few interesting applications are customer shopping traverse pattern discovery, vehicle traveling pattern discovery, and route prediction. Traditional spatial data mining algorithms suitable for the Euclidean space are not directly applicable in these settings. We propose a new algorithm to cluster movement paths such as shopping trips for pattern discovery. In our work, we represent the spatio-temporal series as sequences of discrete locations following a pre-defined network. We incorporate a modified version of the Longest Common Subsequence (LCS) algorithm with the network structure to measure the similarity of movement paths. With such spatial networks we implicitly address the existence of spatial obstructs as well. Experiments were performed on both hand-collected real-life trips and simulated trips in grocery shopping. The initial evaluation results show that our proposed approach, called Net-LCSS, can be used to support effective and efficient clustering for shopping trip pattern discovery.

Original languageFrench
Title of host publicationICIS 2008 Proceedings - Twenty Ninth International Conference on Information Systems
StatePublished - 2008
Event29th International Conference on Information Systems, ICIS 2008 - Paris, France
Duration: Dec 14 2008Dec 17 2008

Other

Other
CountryFrance
CityParis
Period12/14/0812/17/08

Fingerprint

Radio frequency identification (RFID)
Data mining
Global positioning system
Sensors
Experiments

Keywords

  • Path clustering algorithms
  • RFID application
  • Shopping behavior study

ASJC Scopus subject areas

  • Information Systems

Cite this

Yan, P., & Zeng, D. (2008). Créer des clusters de trajectoires d'acheteurs par les structures de réseau. In ICIS 2008 Proceedings - Twenty Ninth International Conference on Information Systems

Créer des clusters de trajectoires d'acheteurs par les structures de réseau. / Yan, Ping; Zeng, Dajun.

ICIS 2008 Proceedings - Twenty Ninth International Conference on Information Systems. 2008.

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

Yan, P & Zeng, D 2008, Créer des clusters de trajectoires d'acheteurs par les structures de réseau. in ICIS 2008 Proceedings - Twenty Ninth International Conference on Information Systems. 29th International Conference on Information Systems, ICIS 2008, Paris, France, 12/14/08.
Yan P, Zeng D. Créer des clusters de trajectoires d'acheteurs par les structures de réseau. In ICIS 2008 Proceedings - Twenty Ninth International Conference on Information Systems. 2008
Yan, Ping ; Zeng, Dajun. / Créer des clusters de trajectoires d'acheteurs par les structures de réseau. ICIS 2008 Proceedings - Twenty Ninth International Conference on Information Systems. 2008.
@inproceedings{199233a3666d4613a5939d4ba38c4ff0,
title = "Cr{\'e}er des clusters de trajectoires d'acheteurs par les structures de r{\'e}seau",
abstract = "Moving objects can be tracked with sensors such as RFID tags or GPS devices. Their movement can be represented as sequences of time-stamped locations. Studying such spatio-temporal movement sequences to discover spatial sequential patterns holds promises in many real-world settings. A few interesting applications are customer shopping traverse pattern discovery, vehicle traveling pattern discovery, and route prediction. Traditional spatial data mining algorithms suitable for the Euclidean space are not directly applicable in these settings. We propose a new algorithm to cluster movement paths such as shopping trips for pattern discovery. In our work, we represent the spatio-temporal series as sequences of discrete locations following a pre-defined network. We incorporate a modified version of the Longest Common Subsequence (LCS) algorithm with the network structure to measure the similarity of movement paths. With such spatial networks we implicitly address the existence of spatial obstructs as well. Experiments were performed on both hand-collected real-life trips and simulated trips in grocery shopping. The initial evaluation results show that our proposed approach, called Net-LCSS, can be used to support effective and efficient clustering for shopping trip pattern discovery.",
keywords = "Path clustering algorithms, RFID application, Shopping behavior study",
author = "Ping Yan and Dajun Zeng",
year = "2008",
language = "French",
booktitle = "ICIS 2008 Proceedings - Twenty Ninth International Conference on Information Systems",

}

TY - GEN

T1 - Créer des clusters de trajectoires d'acheteurs par les structures de réseau

AU - Yan, Ping

AU - Zeng, Dajun

PY - 2008

Y1 - 2008

N2 - Moving objects can be tracked with sensors such as RFID tags or GPS devices. Their movement can be represented as sequences of time-stamped locations. Studying such spatio-temporal movement sequences to discover spatial sequential patterns holds promises in many real-world settings. A few interesting applications are customer shopping traverse pattern discovery, vehicle traveling pattern discovery, and route prediction. Traditional spatial data mining algorithms suitable for the Euclidean space are not directly applicable in these settings. We propose a new algorithm to cluster movement paths such as shopping trips for pattern discovery. In our work, we represent the spatio-temporal series as sequences of discrete locations following a pre-defined network. We incorporate a modified version of the Longest Common Subsequence (LCS) algorithm with the network structure to measure the similarity of movement paths. With such spatial networks we implicitly address the existence of spatial obstructs as well. Experiments were performed on both hand-collected real-life trips and simulated trips in grocery shopping. The initial evaluation results show that our proposed approach, called Net-LCSS, can be used to support effective and efficient clustering for shopping trip pattern discovery.

AB - Moving objects can be tracked with sensors such as RFID tags or GPS devices. Their movement can be represented as sequences of time-stamped locations. Studying such spatio-temporal movement sequences to discover spatial sequential patterns holds promises in many real-world settings. A few interesting applications are customer shopping traverse pattern discovery, vehicle traveling pattern discovery, and route prediction. Traditional spatial data mining algorithms suitable for the Euclidean space are not directly applicable in these settings. We propose a new algorithm to cluster movement paths such as shopping trips for pattern discovery. In our work, we represent the spatio-temporal series as sequences of discrete locations following a pre-defined network. We incorporate a modified version of the Longest Common Subsequence (LCS) algorithm with the network structure to measure the similarity of movement paths. With such spatial networks we implicitly address the existence of spatial obstructs as well. Experiments were performed on both hand-collected real-life trips and simulated trips in grocery shopping. The initial evaluation results show that our proposed approach, called Net-LCSS, can be used to support effective and efficient clustering for shopping trip pattern discovery.

KW - Path clustering algorithms

KW - RFID application

KW - Shopping behavior study

UR - http://www.scopus.com/inward/record.url?scp=84870956676&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84870956676&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84870956676

BT - ICIS 2008 Proceedings - Twenty Ninth International Conference on Information Systems

ER -