引用本文: 职露, 余旭初, 李光强. 滚圆法用于空间点聚类的研究[J]. 武汉大学学报 ( 信息科学版), 2018, 43(8): 1193-1198.
ZHI Lu, YU Xuchu, LI Guangqiang. Spatial Point Clustering Analysis Based on the Rolling Circle[J]. Geomatics and Information Science of Wuhan University, 2018, 43(8): 1193-1198.
 Citation: ZHI Lu, YU Xuchu, LI Guangqiang. Spatial Point Clustering Analysis Based on the Rolling Circle[J]. Geomatics and Information Science of Wuhan University, 2018, 43(8): 1193-1198.

## Spatial Point Clustering Analysis Based on the Rolling Circle

• 摘要: 空间点聚类依据空间点实体属性对其进行分类划分，挖掘对研究应用有价值的信息。目前，空间点聚类大多数方法能够发现多边形簇，但不能发现线状簇。针对空间点聚类现有方法在发现线状簇方面的不足，借鉴滚球法的思想，提出滚圆法用于空间点聚类的研究算法（spatial point clustering using the rolling circle，SPCURC）。针对研究区域的点实体，该算法用给定半径的圆从初始点开始按照原则进行滚动，直至满足条件为止；连接滚圆接触的点，从而形成多边形簇或者线状簇。通过模拟算例和实际算例验证了该算法的可行性。

Abstract: As a main tool of spatial data mining, spatial point clustering does offer interesting methods to address data effectively. At present, the study of spatial point clustering is mature and current methods may divide the initial data into different clusters. However, few methods consider geographi-cal features by linear distribution. Here, a novel spatial point clustering using the rolling circle (SPCURC) is proposed, which derives from the rolling sphere method. SPCURC uses a circle with a known radius is used to roll from the initial point to another point. The rolling does not stop until the condition is met. Then, a polygon cluster or a linear cluster will be generated with points contacted by the rolling circle.This paper also introduces the theory, the detailed calculation procedure and the algorithm complexity. In order to verify the proposed method, the simulated and actual experiments have been performed respectively. DBSCAN algorithm and hierarchical clustering method were employed as the comparative clustering methods. With two synthetic data sets, simulated experiments show that SPCURC is superior to the comparative methods in acquiring linear clusters and can get different types of clusters no matter whether the areas are low-density or high-density. With two real datasets, which contain a residential area in the south and some global seismic data, the actual experiments confirm that SPCURC has the advantages and the applicability to find the clusters that are in a linear way by comparison to DBSCAN and hierarchical clustering. The results indicate that the proposed algorithm is feasible, effective and practical providing the linear clusters and the clustering maps tallying with the initial data.

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