Quantitative Research on Traditional Chinese Medicine Syndrome Based on TF-IDF Relative Entropy / 世界科学技术-中医药现代化

ABSTRACT

This study proposed to use Term Frequency - Inverse Document Frequency (TF-IDF) relative entropy as knowledge representation method between symptoms and syndrome. TF-IDF was originated from text mining. It was an important method in the automatic text categorization. TF-IDF also represented the automatic categorization idea in traditional Chinese medicine (TCM) syndrome. It was based on the fact that the higher frequency of one symptom in specific syndrome, the stronger ability to distinguish this syndrome (TF); and the more wide range of one symptom in all syndrome, and the lower ability to distinguish a syndrome (IDF). It was verified with specific examples.