矿山信息的关联规则算法改进研究

Research on Improvement of Association Rule Algorithm for Mine Information

为了分析矿山生产过程中各危险因素的依赖关系及危险源的重要程度,通过对传统Apriori算法的缺点进行分析,提出了一种改进的分布式并行算法。改进算法应用了MapReduce编程模型和HBase存储数据库,可以仅通过一次扫描实现对海量数据的挖掘,并减少了支持度计数的计算,极大提高了数据挖掘的效率,实现了矿山大数据的高效关联分析。

In order to analyze the dependence of various risk factors and the importance of risk sources in the mine production process,proposed an improved distributed parallel algorithm by analyzing the shortcomings of traditional Apriori algorithm.The improved algorithm applies the MapReduce programming model and the HBase storage database,which can realize the mining of massive data through only one scan,and reduce the calculation of support counts.It greatly improves the efficiency of data mining and realizes efficient association analysis of mine big data.