基于FP-growth算法的高校群体性突发事件关联规则分析

为有效预防高校群体性突发事件,借助数据挖掘关联规则挖掘理论,在分析高校突发事件关键诱发因素基础上,构建基于FP-growth算法的高校群体性突发事件关联规则挖掘模型。并将模型应用于事务数据库数据的分析中,研究关键诱发因素间关联关系,实现强关联规则输出。研究结果表明,多数高校群体性突发事件的发生与日期没有必然联系;内部管理因素、内外部突发事件、内部突发事件、政治因素是诱发高校群体性突发事件的主要因素,且外部因素导致的群体性突发事件影响力远远超过内部因素的影响力;当突发事件发生后,应急处置的有效性是决定突发事件影响力的重要因素。

Analysis of University Mass Emergency Association Rules Based on FP-growth Algorithm

In order to prevent university mass emergency effectively,based on related data mining theories and analysis of university mass emergency key inducing factors,a model for university mass emergency association rules mining was built based on FP-growth algorithm.The model was used to analyse data from transactional databases for relationships among the key inducing factors and mining association rules.The results show that there is no direct correlation between most of university mass emergencies and occurring date,that internal management fatcors,internal and external emergency,internal accident,political factors are main cause of university mass emergency,that inducing power of external factors is much greater than that of internal factors,and that the effectiveness of emergency disposal is the important factor determining the consequence of the emergencies.