一种新型的基于密度和栅格的聚类算法*

针对网格和密度方法的聚类算法存在效率和质量问题，给出了密度和栅格相结合的聚类挖掘算法，即基于密度和栅格的聚类算法DGCA（density and grid based clustering algorithm）。该算法首先将数据空间划分为栅格单元，然后把数据存储到栅格单元中，利用DBSCAN密度聚类算法进行聚类挖掘；最后进行聚类合并和噪声点消除，并将局部聚类结果映射到全局聚类结果。实验通过人工数据样本集对该聚类算法进行理论上验证，表明了该算法在时间效率和聚类质量两方面都得到了提高。

Novel clustering algorithm based on grid and density

In view of the efficiency and quality issues existed in both the grid and density clustering algorithms, this paper proposed the combination of density and grid clustering algorithm, that is DGCA (density and grid based clustering algorithm) which based on density and grid. The given algorithm firstly divides data space into grids; followed by storing data into the grid cell, and uses DBSCAN to conduct clustering mining; finally, it carries on clustering merging and elimination of noise points, and maps the local clustering results to the global clustering results. The experiment was theoretically varified with artificial data set on this clustering algorithm, and showed that the algorithm gained enhance on both time efficiency and clustering quality.