1. Trang chủ
  2. » Luận Văn - Báo Cáo

Sử dụng giải thuật squeezer gom cụm dữ liệu chuỗi thời gian dựa vào xu hướng

88 40 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

Thông tin cơ bản

Định dạng
Số trang 88
Dung lượng 1,83 MB

Nội dung

Ngày đăng: 27/01/2021, 08:19

Nguồn tham khảo

Tài liệu tham khảo Loại Chi tiết
[1] C. Aggarwal, A. Hinneburg , D.A. Keim, “On the Surprising Behavior of Distance Metrics in High Dimensional Space.”, Proceedings of the 8th Int’l Conference on Database Theory, London, UK, January 4-6, pp. 420–434, 2001 Sách, tạp chí
Tiêu đề: On the Surprising Behavior of Distance Metrics in High Dimensional Space
[2] R. Agrawal, C. Faloutsos, A. Swami, “Efficient Similarity Search in Sequence Databases,” in Proc. of 4th Int’l. Conf. on Foundation of Data Organization and Algorithms, 1993 Sách, tạp chí
Tiêu đề: Efficient Similarity Search in Sequence Databases
[3] P. Bradley, U. Fayyad, C. Reina, “Scaling Clustering Algorithms to Large Databases.” In: Proceedings of the 4th Int’l Conference on Knowledge Discovery and Data Mining, New York, NY, August 27-31, pp. 9–15, 1998 Sách, tạp chí
Tiêu đề: Scaling Clustering Algorithms to Large Databases
[4] P. Bradley, U. Fayyad, “Refining initial points for k-Means clustering.” In: Proceedings of the 15th International Conference on Machine Learning, Madison, pp. 91–99, 1998 Sách, tạp chí
Tiêu đề: Refining initial points for k-Means clustering
[5] R. Edwards and J. Magge, “Technical Analysis of Stock Trends”, John Magee, Springfield, Massachsetts, 1969 Sách, tạp chí
Tiêu đề: Technical Analysis of Stock Trends
[7] J. Han, M. Kamber, “Data Mining: Concepts and Techniques”, Second Edition, Morgan Kaufmann Publishers, 2006 Sách, tạp chí
Tiêu đề: Data Mining: Concepts and Techniques
[8] Z. He, X. Xu and S. Deng, “Squeezer: An Effcient Algorithm for Clustering Categorical Data”, J. Computer Science and Technology, Vol.17, No 5, pp. 611-624, 2002 Sách, tạp chí
Tiêu đề: Squeezer: An Effcient Algorithm for Clustering Categorical Data
[9] Z. Huang “A Fast Clustering Algorithm to Cluster Very Large Categorical Data Sets in Data Mining”, Proceedings of the SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery, pp. 1–8, 1997 Sách, tạp chí
Tiêu đề: A Fast Clustering Algorithm to Cluster Very Large Categorical Data Sets in Data Mining
[10] E. Keogh, J. Lin, W. Truppel, “Clustering of Time Series Subsequences is Meaningless: Implications for Past and Future Research.” In : The 3rd IEEE International Conference on Data Mining, Melbourne, FL, USA, pp. 19–22, 2003 Sách, tạp chí
Tiêu đề: Clustering of Time Series Subsequences is Meaningless: Implications for Past and Future Research
[11] E. Keogh, M. Pazzani, “An Enhanced Representation of Time Series Which Allows Fast and Accurate Classification, Clustering and Relevance Feedback.” In: Proceedings of the 4th Int’l Conference on Knowledge Discovery and Data Mining, August 27-31, pp. 239–241,1998 Sách, tạp chí
Tiêu đề: An Enhanced Representation of Time Series Which Allows Fast and Accurate Classification, Clustering and Relevance Feedback
[12] T. W. Liao, “Clustering of time series data a survey”, Pattern Recognition, no. 38, pp. 1857 – 1874, 2005 Sách, tạp chí
Tiêu đề: Clustering of time series data a survey
[13] J. Lin, M. Vlachos, E. Keogh, D. Gunopulos, “Iterative Incremental clustering”, DEBT, Grete, Gveece, pp. 14-18, 2004 Sách, tạp chí
Tiêu đề: Iterative Incremental clustering
[14] A. Raudys, E. Malčius, and V. Lenčiauskas, “Moving Averages for Financial Data Smoothing”, Springer-Verlag Berlin Heidelberg, ICIST, CCIS 403, pp. 34–45, 2013 Sách, tạp chí
Tiêu đề: Moving Averages for Financial Data Smoothing
[15] Q. Yang and X. Wu, “10 challenging problems in Data Mining Research”, International Journal of Information Technology & Decision Making, World Scientific Publishing Company, 2006 Sách, tạp chí
Tiêu đề: 10 challenging problems in Data Mining Research
[16] J. Yoon, J. Lee, and S. Kim, “Trend Similarity and Prediction in Time- Series Databases”, SPIE Conference on Data Mining and Knowledge Discovery:Theory, Tools, and Technology II, pp. 201-212, 2000 Sách, tạp chí
Tiêu đề: Trend Similarity and Prediction in Time-Series Databases
[17] J. P. Yoon, Y. Luo and J. Nam, “A Bitmap Approach to Trend Clustering for Prediction in Time-Series Databases”, Data Mining and Knowledge Discovery: Theory, Methods and Technology III, Vol. 4384, pp. 302-312, 2001 Sách, tạp chí
Tiêu đề: A Bitmap Approach to Trend Clustering for Prediction in Time-Series Databases
[18] “Linear extrapolation”, From Wikipedia, the free encyclopedia https://en.wikipedia.org/wiki/Extrapolation , last modified on 12 November 2015, at 12:57 Sách, tạp chí
Tiêu đề: Linear extrapolation
[23] Maximum number of columns for a datagridview, https://social.msdn.microsoft.com/Forums/vstudio/en-US/8fd7cb48-176b-44c1-a0a8-d8f1b44d8e9c/maximum-number-of-columns-for-a-datagridview?forum=csharpgeneral , access on 24/11/2015 Link
[24] How to add more than 65,535 columns in a data grid view in C# windows application? http://stackoverflow.com/questions/1068982/how-to-add-more-than-65-535-columns-in-a-data-grid-view-in-c-sharp-windows-appli , access on 24/11/2015 Link
[26] Historical Data for S&P 500 Stocks, http://pages.swcp.com/stocks/ Access on 24/11/2015 Link

TỪ KHÓA LIÊN QUAN

TÀI LIỆU CÙNG NGƯỜI DÙNG

TÀI LIỆU LIÊN QUAN