数据挖掘技术在铁路治安工作中的应用

The Application of Data Mining Technique on Railway Public Security

在获取铁路车站治安信息的基础上,利用典型的关联规则算法Apriori及其改进算法AprioriTid,对信息库中的数据进行了分析和验证,得出在治安违法案件中人、事、物之间的相互关系并以此预测未来铁路车站的治安情况。以指导车站治安管理中对重点时段、重点区段和重点案情的警力安排,使得在警力紧缺的情况下,科学、合理地安排警力。同时,笔者也对两种算法的效率进行了比较,AprioriTid无论是在算法的精度还是时间复杂度方面都要优于Apriori算法。

Based on the obtaining railway public safety information, this paper uses the typical association rule extracting algorithms-Apriori and improved algorithm AprioriTid, for analyzing and verifying. The mutual relations among the humans, events and matters in the violation cases are extracted, which can be used to predict the future public order situation in railway stations. The information can also be used to guide the police management to the key time interval, key district and key cases, so the police can be arranged scientifically and reasonably to overcome the shortage of police manpower. At the same time, the authors also compare the efficiency of the two algorithms, and find AprioriTid is better than Apriori algo- rithm in respect of both accuracy and time complexity.