1. Trang chủ
  2. » Thể loại khác

Ứng dụng phân tích mẫu chuỗi tuần tự vào việc phát hiện thói quen sử dụng các ứng dụng trên thiết bị di động : Luận văn ThS. Công nghệ thông tin: 60 48 05

73 29 0

Đang tải... (xem toàn văn)

Tài liệu hạn chế xem trước, để xem đầy đủ mời bạn chọn Tải xuống

THÔNG TIN TÀI LIỆU

ckler Lite : 241 Finished mining, total time = 117 ============================== Hình B.48 Kết mining theo nDays = 10, minSup =20% 70 TÀI LIỆU THAM KHẢO [1] Hermersdorf, M Nyholm, H Perkio, J Tuulos, V “Sensing in Rich Bluetooth Environments”- Workshop on WorldSensorWeb, in Proc SenSys, 2006 - sensorplanet.org [2] Eagle, N Pentland, A “Reality mining: sensing complex social systems” Personal and Ubiquitous Computing 2006 – Springer, Vol 10, # 4, 255268 [3] Farrahi, K Gatica-Perez, D “Daily Routine Classification from Mobile Phone Data” In: Popescu-Belis, A., Stiefelhagen, R (eds.) MLMI 2008 LNCS, vol 5237, pp 173–184 Springer, Heidelberg (2008) [4] Farrahi, K Gatica-Perez, D “What did you today? Discovering daily routines from Large-Scale Mobile Data”.In: MM 2008: Proceeding of the 16th ACM International Conference on Multimedia, pp 849–852 ACM, New York (2008) [5] Human Behaviour Analysis Using DataCollected from Mobile Devices International Journal on Advances in Life Sciences, vol no & 2, year 2012, [6] Hara, K Omori, T Ueno, R “Detection of unusual human behaviour in intelligent house”; Proceedings of the 2002 12th IEEE workshop on Neural Networks for Signal Processing, pp 697-706, 2002 [7] Yiping, T Zhiying, Z Hui, G.Huiqiang, L Wei, W Gang, X “Elder Abnormal Activity Detection by Data Mining”, SICE Annual Conference in Sapporo, August 4-6, 2004, vol 1, pp 837–840 (2004) Japan [8] Wren, C Ivanov, Y Kaur, I Leigh, D Westhues, J “SocialMotion: Measuring the Hidden Social Life of a Building” In: J Hightower, B Schiele, and T Strang, (eds.) LoCA 2007 LNCS, vol 4718, pp 85–102 Springer, Heidelberg (2007) [9] McCowan, I Gatica-Perez, D Bengio, S Lathoud, G “Automatic Analysis of Multimodal Group Actions in Meetings”.IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI) 27(3), 305–317 (2005) [10] Vukovic, M Lovrek, I Jevtic, D “Predicting user movement for advanced location-aware services”.In 15th International Conference on Software, Telecommunications and Computer Networks, pp 1–5 SoftCOM 2007, 2007 [11] Azam, M A Tokarchuk, L Adeel, M “Human Behaviour detection Using GSM Location Patterns and Bluetooth Proximity Data” The Fourth International Conference on Mobile Ubiquitous Computing,Services and Technologies, pp 428-433, Florence, Italy, 2010 [12] Yang, J., Wang, W., and Yu, P S 2001 Infominer: mining surprising periodic patterns In Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining ACM Press 71 [13]Agrawal, R and Srikant, R 1995 Mining sequential patterns In Eleventh International Conference on Data Engineering, P S Yu and A S P Chen, Eds IEEE Computer Society Press, Taipei, Taiwan, 3-14 [14] http://www.philippe-fournier-viger.com/spmf/ [15] Han, J and Kamber, M 2000 Data Mining Concepts and Techniques Morgan Kanufmann [16] Srikant, R and Agrawal, R 1996 Mining sequential patterns: Generalizations and performance improvements In Proc 5th Int Conf Extending Database Technology, EDBT, P M G Apers, M Bouzeghoub, and G Gardarin, Eds Vol 1057 Springer-Verlag, 3-17 [17] J., Pei, J., Mortazavi-Asl, B., Chen, Q., Dayal, U., and Hsu, M.-C 2000 FreeSpan: fre-quent pattern-projected sequential pattern mining In Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining ACM Press, 355-359 [18] Pei, J., Han, J., Pinto, H., Chen, Q., Dayal, U., and Hsu, M C 2001 PrefixSpan: Mining sequential patterns efficiently by prefix-projected pattern growth Int Conf on Data Engineering [19] Garofalakis, M N., Rastogi, R., and Shim, K 1999 Spirit: Sequential pattern mining with regular expression constraints In VLDB'99, Proceedings of 25th International Conference on Very Large Data Bases, September 7-10, 1999, Edinburgh, Scotland, UK, M P Atkinson, M E Orlowska, P Valduriez, S B Zdonik, and M L Brodie, Eds Morgan Kaufmann, 223-234 [20] Lin, M.-Y and Lee, S.-Y 2002 Fast discovery of sequential patterns by memory indexing In Proc of 2002 DaWaK 150-160 [21] Zaki, M J 2001 SPADE: An efecient algorithm for mining frequent sequences Machine Learn-ing 42, 1/2, 31-60 [22] Jay Ayres, Johannes Gehrke, Tomi Yiu, and Jason Flannick SPAM: Sequential PAttern Mining using A Bitmap Representation SIGKDD ’02 Edmonton, Alberta, Canada 2002 [23] H Cao, T Bao, Q Yang, E Chen, and J Tian An effective approach for mining mobile user habits In Proceedings of the 19th ACM Conference on Information and Knowledge Management (CIKM’10), pages 1677–1680, 2010 72

Ngày đăng: 23/09/2020, 22:45

Xem thêm:

Mục lục

    DANH MỤC CÁC HÌNH VẼ

    DANH MỤC CÁC KÍ HIỆU VÀ CHỮ VIẾT TẮT

    DANH MỤC CÁC BẢNG

    Chương 1 - GIỚI THIỆU

    Chương 2 - TỔNG QUAN KHAI PHÁ MẪU CHUỖI TUẦN TỰ

    2.1. Đặc điểm của dữ liệu

    2.2. Khai phá dữ liệu mẫu chuỗi tuần tự

    2.2.2. Bài toán Khai phá mẫu chuỗi tuần tự

    2.2.3. Một số thuật toán khai phá mẫu tuần tự

    Chương 3 - MÔ HÌNH ĐỀ XUẤT

TÀI LIỆU CÙNG NGƯỜI DÙNG

TÀI LIỆU LIÊN QUAN

w