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Mining Cluster-Based Temporal Mobile Sequential Patterns in Location-Based Service Environments
June 2011 (vol. 23 no. 6)
pp. 914-927
ASCII Text
x 
 
Eric Hsueh-Chan Lu, Vincent S. Tseng, Philip S. Yu, "Mining Cluster-Based Temporal Mobile Sequential Patterns in Location-Based Service Environments," IEEE Transactions on Knowledge and Data Engineering, vol. 23, no. 6, pp. 914-927, June, 2011.
 
 
BibTex
x
 
@article{ 10.1109/TKDE.2010.155,
author = {Eric Hsueh-Chan Lu and Vincent S. Tseng and Philip S. Yu},
title = {Mining Cluster-Based Temporal Mobile Sequential Patterns in Location-Based Service Environments},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {23},
number = {6},
issn = {1041-4347},
year = {2011},
pages = {914-927},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2010.155},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Knowledge and Data Engineering
TI - Mining Cluster-Based Temporal Mobile Sequential Patterns in Location-Based Service Environments
IS - 6
SN - 1041-4347
SP914
EP927
EPD - 914-927
A1 - Eric Hsueh-Chan Lu,
A1 - Vincent S. Tseng,
A1 - Philip S. Yu,
PY - 2011
KW - Data mining
KW - transportation
KW - mining methods and algorithms
KW - mobile environments.
VL - 23
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 
Eric Hsueh-Chan Lu, National Cheng Kung University, Taiwan
Vincent S. Tseng, National Cheng Kung University, Taiwan
Philip S. Yu, University of Illinois at Chicago, Chicago
Researches on Location-Based Service (LBS) have been emerging in recent years due to a wide range of potential applications. One of the active topics is the mining and prediction of mobile movements and associated transactions. Most of existing studies focus on discovering mobile patterns from the whole logs. However, this kind of patterns may not be precise enough for predictions since the differentiated mobile behaviors among users and temporal periods are not considered. In this paper, we propose a novel algorithm, namely, Cluster-based Temporal Mobile Sequential Pattern Mine (CTMSP-Mine), to discover the Cluster-based Temporal Mobile Sequential Patterns (CTMSPs). Moreover, a prediction strategy is proposed to predict the subsequent mobile behaviors. In CTMSP-Mine, user clusters are constructed by a novel algorithm named Cluster-Object-based Smart Cluster Affinity Search Technique (CO-Smart-CAST) and similarities between users are evaluated by the proposed measure, Location-Based Service Alignment (LBS-Alignment). Meanwhile, a time segmentation approach is presented to find segmenting time intervals where similar mobile characteristics exist. To our best knowledge, this is the first work on mining and prediction of mobile behaviors with considerations of user relations and temporal property simultaneously. Through experimental evaluation under various simulated conditions, the proposed methods are shown to deliver excellent performance.

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Index Terms:
Data mining, transportation, mining methods and algorithms, mobile environments.
Citation:
Eric Hsueh-Chan Lu, Vincent S. Tseng, Philip S. Yu, "Mining Cluster-Based Temporal Mobile Sequential Patterns in Location-Based Service Environments," IEEE Transactions on Knowledge and Data Engineering, vol. 23, no. 6, pp. 914-927, June 2011, doi:10.1109/TKDE.2010.155
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