【摘要】 为了更深入地研究和利用激增的煤炭隐患数据,对某煤矿的隐患及其属性进行了研究、分析与分层,构建了属性的星形全连接模型;并通过数据清洗、概化及连续属性离散化等数据挖掘技术,将大量原始隐患数据转化为适用挖掘的数据。应用经剪枝和连接步的优化改进的Apriori算法,对该煤矿近两年的物态隐患数据记录进行挖掘,得到频繁项集,导出关联规则;最后利用SQL Server 2008数据库和VS2010平台,构建并实现了煤矿物态隐患信息挖掘系统。更多还原

【Abstract】 For in-depth research and use the increasing coal mine hidden danger data, a coal mine hidden danger and its properties are defined and layered, a star schema whole connection of properties is constructed. Then put a great deal of original data which is not applicable for mining converted to qualified through the data cleaning, generalized and continuous attribute discretization based on data mining technology. Using the improved Apriori algorithm, whose efficiency increased by optimizing of pruning and connection step, frequent itemsets and derive association rules are obtained after mining the hidden danger of state data record over the last two years of the coal mine. Finally design and develop mine hidden danger of state information mining system.更多还原