中文摘要：

作为空间数据挖掘技术中的一种,带有噪声的空间聚类应用算法（DBSCAN算法）是基于密度的聚类算法,其可以从空间数据库中发现任意形状的聚类。本文研究了基于密度的空间聚类算法优化原理及实现过程,分析了原始DBSCAN算法存在的问题,通过避免公共领域对象的重复查询,减少对核心对象邻域查询的计算,优化后算法的时间效率提高了33.73%。将优化后的DBSCAN算法应用于村镇网格化管理,可对网格化管理系统中的数据记录进行有效挖掘,为村镇管理工作提供信息和辅助决策。

英文摘要：

As one of the spatial data mining technologies, DBSCAN algorithm is a density-based clustering algorithm. Since it can find clusters with any forms from the spatial database,DBSCAN algorithm becomes more and more popular. The optimization principle and realization process of densitybased spatial clustering algorithm were studied in detail,and the existing problems of original DBSCAN algorithm were analyzed. By avoiding repeated searches of objects in the public domain,the computation of searches on the neighborhood of core object was reduced,and the time efficiency of the algorithm was improved. After analyzing the distribution of roadside stall business in rural areas,two key parameters,i. e.,Eps and Min Pts,of the algorithm and the searching zone of neighborhood of core object were determined. The experiment results showed that the time efficiency of optimized algorithm was improved by approximately 33. 73%. Finally, the optimized algorithm was applied to the community grid management in rural areas. By data mining of the rural area grid management system,the most frequent regions were successfully identified for roadside stall business. Using this algorithm,the hot spots of problems in rural area management can be found out in time,which uncovered the common rules hidden behind the routine business. Hence,the corresponding management can be performed to a certain region,which can provide information and auxiliary decisions for rural area management.