基于时空密度算法的用户轨迹数据兴趣区域发现

Periodic activity pattern mining base on ST-OPTICS algorithm

在OPTICS(ordering points to identify the clustering structure)算法主要考虑空间信息的基础上,提出了时空密度(STOPTICS)算法,增加了处理噪声孤立点时考虑时间距离的方法,并对每一兴趣区域内部的轨迹点根据时间轴做二度聚类,结合Apriori算法挖掘出用户频繁的行为模式,从而实现对用户兴趣区域及行为模式的挖掘研究。通过微软Geolife数据验证算法有效,为下一步处理用户轨迹数据奠定了基础。

On the basis of ordering points to identify the clustering structure （OPTICS） algorithm, this article puts forward ST- OPTICS algorithm which not only takes into consideration the spatial information but also deals with the time distance while dis-posing the isolated points. Furthermore, we also apply a second-degree clustering of time distance in every single important loca-tion, The Apriori algorithms is applied to mine the frequent activity pattern of users. The effectiveness of this approach is valida-ted base on simulated datasets and real datasets collected in Geolife project. It also provides foundation for future studies on us-ers？ trajectory data mining.