ST-CFSFDP:快速搜索密度峰值的时空聚类算法

时空聚类算法是地理时空大数据挖掘的基础研究命题。针对传统CFSFDP聚类算法无法应用于时空数据挖掘的问题,本文提出一种时空约束的ST-CFSFDP(spatial-temporal clustering by fast search and find of density peaks)算法。在CFSFDP算法基础上加入时间约束,修改了样本属性值的计算策略,不仅解决了原算法单簇集多密度峰值问题,且可以区分并识别相同位置不同时间的簇集。本文利用模拟时空数据与真实的室内定位轨迹数据进行对比试验。结果表明,该算法在时间阈值90 s、距离阈值5 m的识别正确率高达82.4%,较经典ST-DBCSAN、ST-OPTICS及ST-AGNES聚类算法准确率分别提高了5.2%、4.2%和7.6%。

Spatial-temporal clustering by fast search and find of density peaks

Spatial-temporal clustering algorithm is the basic research topic of geographic spatial-temporal big data mining. In view of the problem that traditional CFSFDP clustering algorithm cannot be applied in spatio-temporal data mining, this paper proposes a spatio-temporal constraint algorithm called ST-CFSFDP(spatial-temporal clustering by fast search and find of density peaks). ST-CFSFDP adds time constraint on the basis of CFSFDP algorithm, and modifies the calculation strategy of sample attribute value, which not only solves the problem of multi-density peak of single cluster set in the original algorithm, but also can distinguish and identify clusters at the same location and at different times. In this paper, the simulated spatiotemporal data and real indoor location trajectory data were used for the experiment, the results show that the ST-CFSFDP algorithm has a recognition rate of 82.4% at a time threshold of 90 s and a distance threshold of 5 m,which is better than the classic ST-DBCSAN, ST-OPTICS and ST-AGNES algorithm increased by 5.2%, 4.2%, and 7.6%, respectively.