中文摘要：

[目的]进行细菌性痢疾疫情风险数据挖掘并形成关联规则。[方法]传染病疫情资料来源于辽宁省朝阳市疾。病预防控制中心，收集1981-1994年的伤寒副伤寒、斑疹伤寒、甲型肝炎、乙型肝炎、细菌性痢疾、流行性脑脊髓膜炎、百日咳、猩红热和流行性乙型脑炎等法定传染病发病率数据。气象资料由该市气象站提供，内容包括各年相应的13项月气象指标。首先将：气象指标和传染病数据离散化为计数资料，分为高、中、低3个水平，由此将源数据库映射为挖掘数据库；设置最小支持度为0．15，最小置信度为0．9，利用Apriofi算法进行关联规则挖掘。[结果]共形成70个强关联规则，这些强关联规则中蕴含着细菌性痢疾与季节、气温、气压、降水量、蒸发量等影响因素之间的关联关系。[结论]本方法有利于将抽象的数理统计理论转变为实用的关联规则来指导疾病预防控制实践。

英文摘要：

[Objective] To detect the potential factors which may cause the outbreaks of intectious diseases and estimate their risk and tendency, and thus provide theoretical and technical support for public health administrators. [Methods] Meteo- rological data and infectious disease surveillance data were collected, several pre-processing techniques were explotted, database for mining was constructed by mapping from source data to spreadsheet format file, and then, the method of Apriori algorithm was applied to find all the strong association rules by setting support as 0.15 and confidence as 0.9. [ Results] These associations revealed the relation between risk of Bacillary Dysentery and the influencing factors such as season, air pressure, temperature, precipitation and amount of evaporation. [ Conclusion] The association rules method is proved to be useful in decision-making for infectious disease control.