基于共享单车时空大数据的细粒度聚类

针对传统上单独采用K-means或DBSCAN等方法对共享单车位置数据聚类时造成的聚类结果与真实的聚类结构不符的问题,本文提出了一种基于共享单车时空大数据的细粒度聚类方法(FGCM).该方法通过DBSCAN进行初始聚类,并在此基础上采用GMM-EM算法进行细部聚类,以提取细粒度层级的热点区域.试验表明,该方法可根据密度...

Fine-grained clustering based on spatio-temporal big data of shared bikes

Aiming at the problem that the clustering results obtained by the traditional methods such as K-means and DBSCAN alone when clustering the location data of shared bikes are inconsistent with the real clustering structure, a fine-grained clustering method(FGCM) based on spatio-temporal big data of shared bikes is proposed. This method uses DBSCAN to perform initial clustering, and on this basis, uses GMM-EM algorithm to perform detailed clustering to extract fine-grained hotspots. Experiments show that this method can eliminate noise and outliers based on the density threshold, has no need to specify the number of detailed clusters, and the shape and size of the clusters are more flexible. In the case of clustering the location features of the big data of shared bikes, compared with traditional methods that use K-means or DBSCAN alone, FGCM has a higher degree of refinement, and can fully demonstrate the actual characteristics of shared bikes, which can be used to plan facilities such as electronic fences and helps to regulate the parking of shared bikes without reducing the commuting efficiency.