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VLDB Workshop on Big Graphs Online Querying

VLDB Workshop on Data Management and Analytics for Medicine and Healthcare

Big-O(Q) 2015, DMAH 2015: Biomedical Data Management and Graph Online Querying pp 16–31Cite as

The Study of the Compatibility Rules of Traditional Chinese Medicine Based on Apriori and HMETIS Hypergraph Partitioning Algorithm

Part of the Lecture Notes in Computer Science book series (LNISA,volume 9579)

Abstract

One of the major research contents carried by scholars of Traditional Chinese medical science (TCM) is to discover the compatibility rules of herbs to increase the efficacy in treating certain syndromes. However, up to now, most of the compatibility rules of herbs are based on empirical analyses, which make them hard to study. Since concepts of Big Data and machine learning have been popularized gradually, how to use data mining techniques to effectively figure out core herbs and compatibility rules becomes the main research aspect of TCM informatics. In this paper, the hypergraph partitioning algorithm HMETIS based on Apriori is applied to exploit and analyze clinical data about lung cancer. The result shows that all 15 Chinese herbs obtained by the algorithm accord with the core concepts of the treatment of lung cancer by experienced TCM doctors, namely replenishing nutrition, clearing heat-toxin, resolving phlegm and eliminating pathogenic factors.

Keywords

  • Data mining
  • Compatibility rules of herbs
  • Apriori algorithm
  • Hypergraph
  • Community partitioning
  • HMETIS

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Acknowledgment

This work was supported by National Natural Science Foundation of China (Grant No. 61301028); Natural Science Foundation of Shanghai China (Grant No. 13ZR1402900); Doctoral Fund of Ministry of Education of China (Grant No. 20120071120016).

Author information

Authors and Affiliations

  1. Shanghai University of T.C.M., No. 1200, Cailun Road, Shanghai, 201210, China

    Miao Wang, Qiang Zhou & Lijuan Che

  2. Department of Electronic Engineering, Fudan University, No. 220, Handan Road, 200433, Shanghai, China

    Jiayun Li, Li Chen, Yanjun Huang & Huiliang Shang

Authors
  1. Miao Wang
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  2. Jiayun Li
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  3. Li Chen
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  4. Yanjun Huang
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  5. Qiang Zhou
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  6. Lijuan Che
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  7. Huiliang Shang
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Corresponding author

Correspondence to Huiliang Shang .

Editor information

Editors and Affiliations

  1. Stony Brook University, Stony Brook, New York, USA

    Fusheng Wang

  2. University of Utah, Salt Lake City, Utah, USA

    Gang Luo

  3. Columbia University, New York, New York, USA

    Chunhua Weng

  4. Nanyang Technological University, Singapore, Singapore

    Arijit Khan

  5. Qatar Computing Research Institute, Doha, Qatar

    Prasenjit Mitra

  6. Google Research, New York, New York, USA

    Cong Yu

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Wang, M. et al. (2016). The Study of the Compatibility Rules of Traditional Chinese Medicine Based on Apriori and HMETIS Hypergraph Partitioning Algorithm. In: Wang, F., Luo, G., Weng, C., Khan, A., Mitra, P., Yu, C. (eds) Biomedical Data Management and Graph Online Querying. Big-O(Q) DMAH 2015 2015. Lecture Notes in Computer Science(), vol 9579. Springer, Cham. https://doi.org/10.1007/978-3-319-41576-5_2

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  • DOI: https://doi.org/10.1007/978-3-319-41576-5_2

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