基于关联规则的中医症状组团分析

症状组团分析是中医学研究的热点问题，具有重要的理论意义和临床应用价值，也是中医诊断进一步发展的基础，目前尚处于探索阶段。本文以数据挖掘为技术手段，提出了基于关联规则的中医症状组团分析算法，该算法通过分析证素与证候、证候与症状的关联关系，得出症状与症状之间的联系，从而自动发现具有相似或相同意义的症状组团。充分的实验结果表明，所提出的算法可以有效地发现症状组团，准确率达到85.11%。

Analysis of symptom groups in TCM based on association rules

Analysis of Symptom Groups is one of the hot research topics in TCM (Traditional Chinese Medicine) and is now at the stage of exploration. It is the most fundamental problem for TCM diagnosis and significant for TCM theory and clinical practice. To find symptom groups automatically, an algorithm based on association rules is proposed in this paper according to data mining technology. The algorithm discoveries the groups consisting of the symptoms that have same or similar meaning by analyzing relationship between syndrome factors and syndromes, and relationship between syndromes and symptoms. The extensive experiments show that the algorithm could effectively find the symptom groups and its accuracy rate reaches up to 85.11%.