时空轨迹数据挖掘在公安经济群体事件管理中的应用

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时空轨迹数据挖掘在公安经济群体事件管理中的应用

作者：王仲妮靳涛

来源：《现代信息科技》2019年第01期

摘; 要：群体事件预测对群体事件管理具有重要作用。文章通过分析时空轨迹数据的特点，首先确定使用频繁模式对多维度时空轨迹进行数据挖掘，其次对时空轨迹数据进行预处理，最后通过优化Apriori算法，提出一种MapReduce框架下基于Apriori算法的时空轨迹数据挖掘算法，实现在并行运算环境下对时空轨迹数据进行关联规则挖掘。实验表明：该算法可以找出经济群体事件发生的关键因素特征，这些特征值符合不同类型经济事件的特点，为公安行业经济群体事件管理提供决策部署依据。

关键词：群体事件;时空轨迹;Apriori算法;并行运算;数据挖掘

中图分类号：TP311.13; ; ; 文献标识码：A 文章编号：2096-4706（2019）01-0078-03

Application of Spatio-Temporal Trajectory Data Mining in the Police Management

of Economic Group Events

WANG Zhongni1，JIN Tao2

（1. Public Security Department of Shanxi Province，Taiyuan; 030006，China;2.Taiyuan Fire Brigade，Taiyuan; 030006，China）

Abstract：Group event prediction plays an important role in the management of group events. This paper analyzes the characteristics of spatio-temporal trajectory data. Firstly，by comparison，the frequent patterns are the optimal methods for the multi-dimensional spatio-temporal trajectory data mining. Secondly，the trajectory data was pre-processed. Finally，this paper proposes a new spatio-temporal trajectory data mining algorithm based on MapReduce framework. The experiment proves that the new algorithm can find out the key factors which affect the occurrence of economic group events. And the factors are consistent with the characteristics of different types of economic events. It can provide decision-making basis for the management of economic group events.

Keywords：group event;spatial-temporal trajectory;Apriori algorithm;parallel computing;data mining

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