Abstract
As a complementary system to Western medicine, Traditional Chinese Medicine (TCM) provides a unique theoretical and practical approach of treatment to diseases over thousands of years. Accompanying with the increasing number of TCM digital books in digital library, there is an urgent need to explore these resources by the techniques of knowledge discovery. We present a method for creating a network of herbs and partitioning it into groups of related herbs. The method extracts structured information from several TCM digital books, then a new method named Support and Dependency Evaluation (SDE) is presented for herbal combinational rule mining. The herbal network is created from the extracted dataset of paired herbs. The partitioning procedure is designed to extend FEC algorithm to deal with the weighted herbal network. Experiments demonstrate that the method proposed has the capability of discovering groups of related herbs.
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Wang, L., Zhang, Y., Wei, B., Yuan, J., Ye, X. (2011). A Method for Finding Groups of Related Herbs in Traditional Chinese Medicine. In: Tang, J., King, I., Chen, L., Wang, J. (eds) Advanced Data Mining and Applications. ADMA 2011. Lecture Notes in Computer Science(), vol 7120. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25853-4_5
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DOI: https://doi.org/10.1007/978-3-642-25853-4_5
Publisher Name: Springer, Berlin, Heidelberg
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