摘要
个人移动通讯设备和位置感知设备的广泛应用,使得运营商积累了大量的用户位置数据.目前对位置数据的研究大都关注于活动轨迹的挖掘,而少量对于个人驻留规律的研究也只停留在识别出驻留点,却缺乏进一步的挖掘.本文基于基站采集的位置数据进行研究,依据基站数据的特点,提出了一种简单的识别驻留点的方法.继而提出了两种挖掘驻留规律的方法.最后使用真实数据对算法效果进行了验证.
With the widespread use of personal mobile communication devices and location-aware devices, the mobile communication service provider has accumulated a lot of its users＇ location data. At present, most researches on location data are focused on the mining of active trajectories. A small amount of researches on the pattern of personal stay only determine activity stops, but lack further mining. We conduct researches based on the base station data and propose a simple method to identify the activity stops according to the characteristics of the base station data. Then we propose two methods for mining the pattern of personal stay. Finally, the real data are used to verify the effectiveness of the algorithm.
出处
《计算机系统应用》
2017年第9期176-180,共5页
Computer Systems & Applications
基金
国家自然科学基金(91530324)
国家高技术研究发展计划(2015AA01A302)
关键词
基站数据
活动停留
密度聚类
最大频繁项集挖掘算法
base-station data
activity stops
density-based clustering
mining algorithm for maximum frequent itemsets
