目的：基于频繁模式树（Frequent Pattern-tree，FP-tree）增长算法挖掘影响愤怒郁怒人群睡眠质量的强关联因素。方法：设计了构造FP-tree的算法和挖掘频繁项集的算法，采用FP-Tree增长算法，通过状态树记录扫描的数据库信息，通过减少项目集的搜索空间，一次扫描数据库生成满足最小支持度要求的频繁项目集，实现了情志病证数据库对愤怒郁怒人群影响睡眠质量各种强关联因素的挖掘。结果：影响愤怒郁怒人群睡眠质量最频繁的关联因素是呼吸不畅、咳嗽或鼾声高、感觉冷、感觉热或做噩梦，程序分析总的时间是2 s。结论：基于FP-tree的频繁项集挖掘算法能有效实现对情志病证数据库海量数据中有用信息的针对性挖掘。

This article was aimed to study the factors associated with sleep quality of anger-out and anger-in population based on the frequent pattern-tree (FP-Tree) growing algorithm with data mining. The algorithm of structuring frequent model FP-tree and mining frequent itemsets were designed. The database information scanned was recorded by using FP-Tree growing algorithm through state-trees. The frequent itemsets met minimum support required was generated through reducing the search space of project sets and scanning database only one. The data mining of all factors associated with emotional diseases was actualized. The results showed that factors associated with sleep quality of anger-out and anger-in population were disturbance in respiration, cough or snoring, feeling cold, hot or nightmares. The total time for program analysis was 2 seconds. It was concluded that data mining algorithm based on FP-Tree frequent itemsets can effectively realize the useful information receiving from factors associated with emotional diseases.

国家自然科学基金委面上项目（81173151）：雌激素受体β介导的中枢5-HT功能在经前期综合征肝气逆郁两证中的作用，负责人：乔明琦。