数学与计算机科学 |
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面向用户行为理解的移动通讯数据可视分析 |
蒋宏宇1, 吴亚东1,2, 赵韦鑫1, 唐楷1 |
1. 西南科技大学 计算机科学与技术学院, 四川 绵阳 621010; 2. 西南科技大学 四川省军民融合研究院, 四川 绵阳 621010 |
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Mobile data visual analysis for human activity understanding |
JIANG Hongyu1, WU Yadong1,2, ZHAO Weixin1, TANG Kai1 |
1. Southwest University of Science and Technology, Mianyang 621010, Sichuan Province, China; 2. Sichuan Civil-Military Integration Institute, Southwest University of Science and Technology, Mianyang 621010, Sichuan Province, China |
引用本文:
蒋宏宇, 吴亚东, 赵韦鑫, 唐楷. 面向用户行为理解的移动通讯数据可视分析[J]. 浙江大学学报(理学版), 2018, 45(1): 37-43.
JIANG Hongyu, WU Yadong, ZHAO Weixin, TANG Kai. Mobile data visual analysis for human activity understanding. Journal of ZheJIang University(Science Edition), 2018, 45(1): 37-43.
链接本文:
https://www.zjujournals.com/sci/CN/10.3785/j.issn.1008-9497.2018.01.007
或
https://www.zjujournals.com/sci/CN/Y2018/V45/I1/37
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[1] CHEN S, YUAN X, WANG Z, et al. Interactive visual discovering of movement patterns from sparsely sampled geo-tagged social media data[J]. IEEE Transactions on Visualization & Computer Graphics, 2016, 22(1):270. [2] KRVGER R, LOHMANN S, THOM D, et al. Using social media content in the visual analysis of movement data[J]. Research Microsoft Com, 2012, 97(11):31-32. [3] ANDRIENKO G, ANDRIENKO N, WROBEL S. Visual analytics tools for analysis of movement data[J].ACM SIGKDD Explorations Newsletter, 2007, 9(2):38-46. [4] CHU D, SHEETS D A, ZHAO Y, et al. Visualizing hidden themes of Taxi movement with semantic transformation[C]//Visualization Symposium (PacificVis). Piscataway:IEEE Press, 2014:137-144. [5] GONZÁLEZ M C, HIDALGO C A, BARABÁSI A L. Understanding individual human mobility patterns[J]. Nature, 2008, 453(7196):779. [6] ZHU Y, ZHANG Y, SHANG W, et al. Trajectory enabled service support platform for mobile users' behavior pattern mining[C]//Mobile and Ubiquitous Systems:NETWORKING & Services. Piscataway:IEEE Press, 2009:1-10. [7] 谭钧元, 宋国杰, 谢昆青, 等. 一种有效的基于生活熵的移动用户分类算法[J]. 计算机研究与发展2009, 46:433-438. TAN J Y, SONG G J, XIE K Q, et al. An effective mining method for mobile subscribers based on life entropy[J]. Journal of Computer Research and Development, 2009, 46:433-438. [8] SHAD S A.移动用户轨迹与行为模式挖掘方法研究[D]. 合肥:中国科学技术大学, 2013. SHAD S A. Mobile User Trajectory and Profile Mining[D]. Hefei:University of Science and Technology of China, 2013. [9] CALABRESE F, PEREIRA F C, LORENZO G D, et al. The geography of taste:Analyzing cell-phone mobility and social events[J]. Lecture Notes in Computer Science, 2010, 6030:22-37. [10] PULSELLI R M, ROMANO P, RATTI C, et al. Computing urban mobile landscapes through monitoring population density based on cell-phone chatting[J]. International Journal of Design & Nature & Ecodynamics, 2008, 3(2):121-134. [11] READES J, CALABRESE F, SEVTSUK A, et al. Cellularcensus:Explorations in urban data collection[J]. IEEE Pervasive Computing, 2007, 6(3):30-38. [12] DE MONTJOYE Y A, HIDALGO C A, VERLEYSEN M, et al. Unique in the crowd:The privacy bounds of human mobility[J]. Scientific Reports, 2013, 3(6):1376. [13] WU W, XU J, ZENG H, et al. TelCoVis:Visual exploration of co-occurrence in urban human mobility based on telco data[J].IEEE Transactions on Visualization & Computer Graphics, 2016, 22(1):935. [14] GONZÁLEZ M C, HIDALGO C A, BARABÁSI A L. Understanding individual human mobility patterns[J].Nature, 2008, 453(7196):779. [15] ANDRIENKO G, ANDRIENKO N, FUCHS G.Business Intelligence[M]. Berlin:Springer International Publishing, 2015:39-59. [16] ARIETTA S M, EFROS A A, RAMAMOORTHI R, et al. City forensics:Using visual elements to predict non-visual city attributes[J].IEEE Transactions on Visualization and Computer Graphics, 2014, 20(12):2624-2633. |
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