时空关联规则挖掘算法及其在ITS中的应用  

Spatio-temporal Association Rule Mining Algorithm and its Application in Intelligent Transportation System

作　　者：夏英[1,2] 张俊[2] 王国胤[2] 

机构地区：[1]西南交通大学信息科学与技术学院,成都610031 [2]重庆邮电大学计算机学院,重庆400065

出　　处：《计算机科学》2011年第9期173-176,共4页Computer Science

基　　金：国家自然科学基金(60773113);重庆市计算机网络与通信技术重点实验室开放基金项目(CY-CNCL-2009-01);重庆市科委科技项目(CSTC2009CB2015)资助

摘　　要：同时考虑时间和空间约束,能够在分析过程中及时过滤不相关的数据,提高时空关联规则的获取效率。基于这一思路,在频繁项集的产生过程中同时分析数据的时间有效性和空间关联性,提出了Spatio-Temporal Apriori(STApriori)算法。算法首先对时空数据进行时间段划分和空间关联性分析并形成事务表,然后对空间关联的项集进行连接并产生时空关联规则。实验表明了算法的有效性。该算法在智能交通系统(ITS)的应用,可以利用路段间的时空关联规则分析交通拥堵趋势。Taking into account the spatial and temporal constraints simultaneously can filter irrelevant data early and improve the efficiency of discovering spatio-temporal association rule.Based on the idea,Spatio-Temporal Apriori（STApriori） algorithm was proposed.It analyzes the time validity and spatial relativity at the same time during the gene-ration of frequency item sets.It classifies the time duration of spatio-temporal data and considers the spatial relationship firstly and generates the transaction table,then performs join operation on spatial-related item sets.Experiments illuminate that the algorithm is well performed.The algorithm is applied in intelligent transportation system to analyze the trend of traffic congestion by identifying spatio-temporal association between road sections.

关 键 词：时空约束 关联规则 交通拥堵趋势 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]