基于停留时间的语义行为模式挖掘

发布时间：2018-07-14 14:50

【摘要】：移动对象的语义行为模式挖掘是当前移动对象研究中关注的热点,有益于诸多应用场景,如朋友推荐系统、轨迹破案领域和个性化服务等.目前语义行为模式挖掘方法没有考虑移动对象在停留点的停留时间,不能准确地分辨出移动对象之间的不同行为模式.为了解决上述问题,提出了一种基于停留时间的语义行为模式挖掘(discovering common behavior using staying duration on semantic trajectory,DSTra)方法,首先挖掘每个移动对象的频繁语义行为模式,然后定义语义行为模式之间的相似性度量方法,最后采用层次聚类的方法对移动对象进行聚类,找出具有相似行为模式的移动对象群体.实验结果表明:该方法不仅具有合理性和有效性,同时还具有较高的准确率和较好的效率.
[Abstract]:The semantic behavior pattern mining of moving objects is a hot topic in the research of moving objects, which is beneficial to many application scenarios, such as friend recommendation system, locus detection field and personalized service. The current semantic behavior pattern mining methods do not consider the residence time of moving objects at the point of stay and can not accurately distinguish the different behavior patterns between moving objects. In order to solve the above problems, a method of mining (discovering common behavior using staying duration on semantic objects and DSTra based on residence time semantic behavior pattern is proposed. Firstly, the frequent semantic behavior patterns of each moving object are mined. Then the similarity measure method between semantic behavior patterns is defined and the moving object group with similar behavior pattern is found by hierarchical clustering method. The experimental results show that this method is not only reasonable and effective, but also has higher accuracy and better efficiency.
【作者单位】：北京工业大学;中国科学院软件研究所;中国科学院大学;
【基金】：国家自然科学基金项目(61402449,91546111,91646201) 中国科学院重点部署项目(KGZD-EW-102-3-3) 北京市教委重点项目(KZ201610005009) 北京工业大学“17内涵发展定额——引进人才科研启动费”~~
【分类号】：TP311.13

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