Pattern Mining, Semantic Label Identification and Movement Prediction Using Mobile Phone Data

详细信息    查看全文

• 作者：Rong Xie (22)
  Jun Luo (23) (24)
  Yang Yue (25) (26)
  Qingquan Li (25) (26)
  Xiaoqing Zou (27)
  
• 关键词：Mobile phone log data ; Mobility pattern ; Frequent pattern mining ; Semantic labels ; Movement prediction
• 刊名：Lecture Notes in Computer Science
• 出版年：2012
• 出版时间：2012
• 年：2012
• 卷：7713
• 期：1
• 页码：431-442
• 全文大小：665KB
• 参考文献：1. Isaacman, S., Becker, R., C谩ceres, R., Kobourov, S., Martonosi, M., Rowland, J., Varshavsky, A.: Identifying Important Places in People鈥檚 Lives from Cellular Network Data. In: Lyons, K., Hightower, J., Huang, E.M. (eds.) Pervasive 2011. LNCS, vol.聽6696, pp. 133鈥?51. Springer, Heidelberg (2011) CrossRef
  2. Licoppe, C., Diminescu, D., Smoreda, Z., Ziemlicki, C.: Using Mobile Phone Geolocalisation for 鈥橲ocio-Geographical鈥?Analysis of Co-ordination, Urban Mobilities, and Social Integration Patterns. Journal of Economic & Social Geography聽99, 584鈥?01 (2008)
  3. Bayir, M.A., Demirbas, M., Eagle, N.: Discovering Spatiotemporal Mobility Profiles of Cellphone Users. In: Proceedings of IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks & Workshops, pp. 1鈥?. IEEE Press (2009)
  4. Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: Proceedings of 20th International Conference on Very Large Data Bases (VLDB), pp. 487鈥?99. Morgan Kaufmann Press (1994)
  5. Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H.: PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth. In: Proceedings of the 17th International Conference on Data Engineering (ICDE), pp. 215鈥?24. IEEE Computer Society (2001)
  6. Birant, D., Kut, A.: ST-DBSCAN: An Algorithm for Clustering Spatial-Temporal Data. Data & Knowledge Engineering聽60, 208鈥?21 (2007) CrossRef
  7. Gonz谩lez, M.C., Hidalgo, C.A., Barab谩si, A.L.: Understanding Individual Human Mobility Patterns. Nature聽453(5), 779鈥?82 (2008) CrossRef
  8. Eagle, N., Pentland, A.S.: Reality Mining: Sensing Complex Social Systems. Personal and Ubiquitous Computing聽10(4), 255鈥?68 (2006) CrossRef
  9. Phithakkitnukoon, S., Dantu, R.: Mobile Social Closeness and Similarity in Calling Patterns. In: Proceedings of IEEE Conference on Consumer Communications & Networking Conference (CCNC 2010), pp. 1鈥?. IEEE Press (2010)
  10. Phithakkitnukoon, S., Dantu, R.: Predicting Calls 鈥?New Service for an Intelligent Phone. In: Krishnaswamy, D., Pfeifer, T., Raz, D. (eds.) MMNS 2007. LNCS, vol.聽4787, pp. 26鈥?7. Springer, Heidelberg (2007) CrossRef
  11. Phithakkitnukoon, S., Dantu, R.: CPL: Enhancing Mobile Phone Functionality by Call Predicted List. In: Meersman, R., Tari, Z., Herrero, P. (eds.) OTM 2008 Workshops. LNCS, vol.聽5333, pp. 571鈥?81. Springer, Heidelberg (2008) CrossRef
  
• 作者单位：Rong Xie (22)
  Jun Luo (23) (24)
  Yang Yue (25) (26)
  Qingquan Li (25) (26)
  Xiaoqing Zou (27)
  
  22. International School of Software, Wuhan University, Wuhan, 430079, China
  23. Shenzhen Institutes of Advanced Technology, CAS, Shenzhen, 518055, China
  24. Shenzhen Key Laboratory of High Performance Data Mining, Shenzhen, 518055, China
  25. Shenzhen University, Shenzhen, China
  26. State Key Lab of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430079, China
  27. Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming, China
  
• ISSN：1611-3349

文摘

Data collected from mobile phones have potential knowledge to provide with important behavior patterns of individuals. In this paper, we present approaches to discovering personal mobility and characteristics based on mobile phone location information and semantic analysis. We discuss three aspects related to very common mobile phone-related applications such as pattern mining, semantic label identification and movement prediction. We use real mobile phone data to perform functions of discovering these behavior patterns and demonstrate effectiveness of our approaches.