Advertisement

TCMiner: A High Performance Data Mining System for Multi-dimensional Data Analysis of Traditional Chinese Medicine Prescriptions

  • Chuan Li
  • Changjie Tang
  • Jing Peng
  • Jianjun Hu
  • Lingming Zeng
  • Xiaoxiong Yin
  • Yongguang Jiang
  • Juan Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3289)

Abstract

This paper introduces the architecture and algorithms of TCMiner: a high performance data mining system for multi-dimensional data analysis of Traditional Chinese Medicine prescriptions. The system has the following competing advantages: (1) High Performance (2) Multi-dimensional Data Analysis Capability (3) High Flexibility (4) Powerful Interoperability (5) Special Optimization for TCM. This data mining system can work as a powerful assistant for TCM experts by conducting Traditional Chinese Medicine Data Mining such as Computer-Aided Medicine Pairing Analysis, Medicine Syndrome Correlation, Quality and Flavor Trend Analysis, and Principal Components Analysis and Prescriptions Reduction etc.

Keywords

Traditional Chinese Medicine Association Rule Mining Frequent Pattern Task Scheduler Data Mining System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    General Guidelines for Methodologies on Research and Evaluation of Traditional Medicine, http://www.who.int/medicines/library/trm/who-edm-trm-2000-1/who-edm-trm-2000-1.pdf
  2. 2.
    Guste Editors’ Notes on the special issue, http://www.sinica.edu.tw/~jds/preface.pdf
  3. 3.
    Huairen, P.: The First Volume of Great Formula Dictionary of TCM. People’s Medical Publishing House (December 1993)Google Scholar
  4. 4.
    Fujin, D.: The Formula of TCM, June 1995, pp. 248–249. Shanghai Scientific and Technical Publishers (1995)Google Scholar
  5. 5.
    Ming, F., Chuan, L.: Mining Frequent Patterns in an FP-tree Without Conditional FP-tree Generation. Journal of Computer Research and Development 40 (2004)Google Scholar
  6. 6.
    Chuan, L., Ming, F.: A New Algorithm On Multi-Dimensional Association Rules Mining. Journal of Computer Science A Complement, 1–4 (2002)Google Scholar
  7. 7.
    Chuan, L., Ming, F.: Generating Association Rules Based On Threaded Frequent Pattern Tree. Journal of Computer Engineering and Application 4 (2004)Google Scholar
  8. 8.
    Ming, F., Chuan, L.: A Fast Algorithm for Mining Frequent Closed ItemSets. In: Submitted to ICDM 2004Google Scholar
  9. 9.
    Chuan, L.: FAN Ming Research on Single-dimensional Association Mining. Full Paper Data Base of Wanfang NetworkGoogle Scholar
  10. 10.
  11. 11.
  12. 12.
    Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Proc. 2000 ACM-SIGMOD Intl. Conf. on Management of Data, May 2000, pp. 1–12 (2000)Google Scholar
  13. 13.
    Agrawal, R., Srikant, R.: Fast algorithms for Mining association rules. In: Proc. 1994 Int’l Conf. on Very Large Data Bases, September 1994, pp. 487–499 (1994)Google Scholar
  14. 14.
    Pei, J., Han, J., Mao, R.: CLOSET: An Efficient Algorithm for Mining Frequent Closed Itemsets. In: Proc. 2000 ACM-SIGMOD Int. 2000 ACM SIGMOD Intl. Conference on Management of Data, pp. 8–10 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Chuan Li
    • 1
  • Changjie Tang
    • 1
  • Jing Peng
    • 1
  • Jianjun Hu
    • 1
  • Lingming Zeng
    • 1
  • Xiaoxiong Yin
    • 1
  • Yongguang Jiang
    • 2
  • Juan Liu
    • 2
  1. 1.The Data Base and Knowledge Engineering Lab (DBKE)Computer School of Sichuan University 
  2. 2.Chengdu University of Traditional Chinese Medicine 

Personalised recommendations