Mining periodic movement patterns of mobile phone users based on an efficient sampling approach

Applied Intelligence

Volumn 35, Issue 1, 2011, Pages 32-40

  Wang, Yao Te ^a   Cheng, Ju Tzu ^b  

^a PROVIDENCE UNIVERSITY   (Taiwan)

^b NATIONAL TAICHUNG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

Author keywords

Data mining; Periodic maximal promising movement pattern; Sampling; Sequential pattern

Indexed keywords

EFFICIENT SAMPLING; LOCATION-BASED SERVICES; M-COMMERCE; MOBILE-PHONE USERS; MOVEMENT PATTERN; PATH GRAPHS; RANDOM SAMPLE; SAMPLING TECHNIQUE; SEQUENTIAL PATTERNS; TIME INTERVAL; TRAVERSAL ALGORITHMS; VECTOR OPERATIONS;

DATA MINING; MINING; TELECOMMUNICATION EQUIPMENT;

CELLULAR TELEPHONE SYSTEMS;

EID: 79959379824     PISSN: 0924669X     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10489-009-0201-z     Document Type: Article

References (13)

1. Agrawal R, Srikant R (1995) Mining sequential patterns. In: Proceedings of the 11th international conference on data engineering, pp 3-14
2. Anwar F, Petrounias I, Kodogiannis VS, Tasseva V, Peneva D (2008) Efficient periodicity mining of sequential patterns in a post-mining environment. In: Proceedings of the 4th international conference on intelligent systems, pp 16-2 to 16-11
3. D Bao 2008 A generalized model for financial time series representation and prediction Appl Intell 29 1 11 10.1007/s10489-007-0063-1
4. Eberspächer J, Vögel HJ (1999) GSM: switching, services and protocols. Wiley, Chichester
5. MG Elfeky WG Aref AK Elmagarmid 2005 Periodicity detection in time series databases IEEE Trans Knowl Data Eng 17 7 875 887 10.1109/TKDE.2005.114 (Pubitemid 41049862)
6. AJT Lee YT Wang 2003 Efficient data mining for calling path patterns in GSM networks Inf Syst 28 929 948 10.1016/S0306-4379(02)00112-6
7. CH Lee 2007 IMSP: an information theoretic approach for multi-dimensional sequential pattern mining Appl Intell 26 231 242 1128.65113 10.1007/s10489-006-0016-0 (Pubitemid 46589210)
8. Özden B, Ramaswamy S, Silberschatz A (1998) Cyclic association rules. In: Proceedings of the 14th international conference on data engineering, pp 412-421
9. WC Peng MS Chen 2003 Developing data allocation schemes by incremental mining of user moving patterns in a mobile computing system IEEE Trans Knowl Data Eng 15 1 70 85 10.1109/TKDE.2003.1161583
10. VS Tseng KW Lin 2006 Efficient mining and prediction of user behavior patterns in mobile web systems Inf Softw Technol 48 357 369 10.1016/j.infsof. 2005.12.014
11. FJ Velez LM Correia 2002 Mobile broadband services: classification, characterization, and deployment scenarios IEEE Commun Mag 40 4 142 150 10.1109/35.995864 (Pubitemid 34434915)
12. G Yavaş D Katsaros Ö Ulusoy Y Manolopoulos 2005 A data mining approach for location prediction in mobile environments Data Knowl Eng 54 121 146 10.1016/j.datak.2004.09.004 (Pubitemid 40683785)
13. Zhou B, Hui SC, Fong ACM (2006) An effective approach for periodic Web personalization. In: Proceedings of the IEEE/WIC/ACM international conference on web intelligence, pp 284-292