On mining movement pattern from mobile users

被引:42
作者
Taniar, David [1 ]
Goh, John [1 ]
机构
[1] Monash Univ, Sch Business Syst, Clayton, Vic 3800, Australia
来源
INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS | 2007年 / 3卷 / 01期
关键词
mobile data mining; mining mobile data; mobile user data mining; intelligent mobile mining; location technique; network resource management; data mining;
D O I
10.1080/15501320601069499
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In the era in which activities performed by mobile users are tracked through various sensing mechanisms, the movement data collected through these sensors is submitted into a data mining algorithm in order to determine the movement pattern. The movement pattern refers to the pattern that mobile users generally take to move from one base location to another base location through multiple intermediate locations. This paper provides a proposal and case study on how the movement pattern can be extracted from mobile users through transforming the user movement database to the location movement database and subsequently transferred to all algorithm Apriori-like movement pattern (AMP) and movement tree (M-tree). The result is a list of sequences in which mobile users frequently go through that which satisfies min-support and min-confidence. The result of this movement pattern mining exercise opens lip a new future for the prediction of the movement for the individual mobile user.
引用
收藏
页码:69 / 86
页数:18
相关论文
共 34 条
[1]  
AGRAWAL R, 1995, PROC INT CONF DATA, P3, DOI 10.1109/ICDE.1995.380415
[2]  
Agrawal R., 1994, Proceedings of the 20th International Conference on Very Large Data Bases. VLDB'94, P487
[3]  
BAGUI S, 2006, INT J DATA WAREHOUS, V2, P50
[4]  
Chakrabarti S., 1998, Proceedings of the Twenty-Fourth International Conference on Very-Large Databases, P606
[5]  
Chen S. Y., 2005, International Journal of Business Intelligence and Data Mining, V1, P4, DOI 10.1504/IJBIDM.2005.007315
[6]  
CHO M, 2005, J DATA WAREHOUSING M, V1, P56
[7]  
Forlizzi L, 2000, SIGMOD RECORD, V29, P319, DOI 10.1145/335191.335426
[8]  
Forsyth D.R., 2019, Group dynamics, V7th
[9]  
Goh J, 2004, LECT NOTES ARTIF INT, V3215, P795
[10]  
GOH J, 2006, IEEE 20 INT C ADV IN