移动目标关联共现规则挖掘算法研究
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摘要
现行的移动目标行为规律侧重于空间或时间单一维度,且多集中于单移动目标的自身行为。为发现移动目标的关联共现规律,在传统轨迹序贯模式挖掘的基础上,基于常用的时间戳和地点的关联模型,加入任务属性信息,以移动目标在空间上的共同出现模式为基准分析时间上的频繁度,提出一种将序列模式和关联规则应用到移动目标活动数据中的模式挖掘算法。实验结果证明,该算法能有效挖掘移动目标的活动地点关联性及协同共现规律,并且具有较低的算法复杂度,可以为建立移动目标的反应机制提供辅助决策。
The current mobile target behavioral rules mostly focus on the single dimension of space or time,or the self-behavior of a single moving target. In order to find out the association co-occurrence rule of moving targets,based on the traditional trajectory sequential pattern mining,this paper adds task attribute information with commonly used time stamps and place association models,and then takes the co-occurrence pattern of moving objects in space as a benchmark to further analyze the frequency of time,proposes a pattern mining algorithm which applies the association rule and sequence mining to the location and equipment association data of the moving target. Experimental results prove that this algorithm can effectively mine the activity location correlation and the rule of co-occurrence of moving target,and has lower algorithm complexity,which can provide decision-making for the establishment of the reaction mechanism of the moving target.
The current mobile target behavioral rules mostly focus on the single dimension of space or time,or the self-behavior of a single moving target. In order to find out the association co-occurrence rule of moving targets,based on the traditional trajectory sequential pattern mining,this paper adds task attribute information with commonly used time stamps and place association models,and then takes the co-occurrence pattern of moving objects in space as a benchmark to further analyze the frequency of time,proposes a pattern mining algorithm which applies the association rule and sequence mining to the location and equipment association data of the moving target. Experimental results prove that this algorithm can effectively mine the activity location correlation and the rule of co-occurrence of moving target,and has lower algorithm complexity,which can provide decision-making for the establishment of the reaction mechanism of the moving target.
引文
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[2]夏英,温海平,张旭.基于轨迹聚类的热点路径分析方法[J].重庆邮电大学学报(自然科学版),2011,23(5):602-606.
[3]袁冠.移动对象轨迹数据挖掘方法研究[D].徐州:中国矿业大学,2012.
[4]赵越,刘衍珩,余雪岗,等.基于模式挖掘与匹配的移动轨迹预测方法[J].吉林大学学报(工学版),2008,38(5):1125-1130.
[5]赵学健,孙知信,袁源.基于预判筛选的高效关联规则挖掘算法[J].电子与信息学报,2016,38(7):1654-1659.
[6]詹益旺,胡斌杰.基于DVTD的移动用户出行模式识别研究[J].计算机工程,2016,42(7):72-76.
[7]蔡建南,刘启亮,徐枫,等.多层次空间同位模式自适应挖掘方法[J].测绘学报,2016,45(4):475-485.
[8]ALMAOLEGI M,ARKOK B.An improved Apriori algorithm for association rules[J].International Journal on Natural Language Computing,2014,3(1):21-29.
[9]TANK D M.Improved algorithm for mining association rules[J].International Journal of Information Technology and Computer Science,2014,6(7):15-23.
[10]GUDMUNDSSON J,VAN KREVELD M.Computing longest duration flocks in trajectory data[C]//Proceedings of Annual ACM International Symposium on Advances in Geographic Information Systems.Washington D.C.,USA:IEEE Press,2006:35-42.
[11]LI Z,DING B,HAN J,et al.Swarm:mining relaxed temporal moving object clusters[J].Proceedings of the VLDB Endowment,2010,3(1-2):723-734.
[12]CAO H,MAMOULIS N,CHEUNG D W.Mining frequent spatio-temporal sequential patterns[C]//Proceedings of IEEE International Conference on Data Mining.Washington D.C.,USA:IEEE Press,2005:82-89.
[13]CHEN Z,SHEN H T,ZHOU X.Discovering popular routes from trajectories[C]//Proceedings of International Conference on Data Engineering.Washington D.C.,USA:IEEE Press,2011:900-911.
[14]SRIKANT R,AGRAWAL R.Mining sequential patterns:generalizations and performance improvements[C]//Proceedings of International Conference on Extending Database Technology.Berlin,Germany:Springer,1996:1-17.
[15]WANG Y,ZHENG Y,XUE Y.Travel time estimation of a path using sparse trajectories[C]//Proceedingds of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York,USA:ACM Press,2014:25-34.
[16]郭莎.移动目标活动规律挖掘方法研究与设计[D].北京:北方工业大学,2017.
[17]陈鹏.基于用户移动数据的可视化用户行为分析[D].广州:华南理工大学,2016.
[18]张巍,刘峰,滕少华.改进的Prefix Span算法及其在序列模式挖掘中的应用[J].广东工业大学学报,2013,30(4):49-54.
[19]AGRAWAL R,SRIKANT R.Fast algorithms for mining association rules in large databases[C]//Proceedings of IEEE International Conference on Software Engineering and Service Science.Washington D.C.,USA:IEEE Press,2014:487-499.
[20]SRIKANT R,AGRAWAL R.Mining sequential patterns:generalizations and performance improvements[C]//Proceedings of International Conference on Extending Database Technology.Berlin,Germany:Springer,1996:3-17.
[21]ZAKI M J.SPADE:An efficient algorithm for mining frequent sequences[J].Machine Learning,2001,42(1-2):31-60.
[22]吕锋,张炜玮.4种序列模式挖掘算法的特性研究[J].武汉理工大学学报,2006,28(2):57-60.