中文摘要：

针对复杂及带噪声的数据集的聚类问题,提出了一种基于局部密度的网格排序策略（GSS-LD）,并以其作为网格聚类的组织模式。GSS-LD利用聚类的局部性质进行网格单元排序,将基于网格的聚类问题转换为网格的排序问题,运用相对局部密度变化率的概念,克服了传统网格聚类算法中全局性参数的局限性,使其可以适应多密度数据集的聚类。通过三组具有不同拓扑结构的数据集测试GSS-LD的聚类性能并与其他两种方法进行比较,结果表明GSS-LD可以对复杂数据集进行有效聚类,其时间复杂度分别与数据规模及网格结构具有线性关系,同时具有较强的噪声处理能力。

英文摘要：

In the process of clustering, numerous complex data appear with noise. This paper proposed a clustering method which utilized a grid scheduling strategy based on local density （ GSS-LD ）. Firstly, GSS-LD used the local properties of clus- tering sorting grid cell, and transformed the grid clustering problem into grid scheduling problem. Secondly, it used the con- cept of relative local density gradient to overcome the limitations of global parameters in conventional clustering algorithm. Thus GSS-LD held a strong ability to deal with the clustering of multi-density data set with noise. The comparing results on 3 data sets with other 2 methods show that GSS-LD is feasible and to handle with the clustering for multi-density data. Mean- while, time complexity of GSS-LD has a linear relation with the scale of data set.