聚类算法能从空间数据库中直接发现一些有意义的聚类结构而不需要背景知识，是空间数据发掘和知识发现的重要手段。在分析已有聚类算法的基础上，提出了一种基于数学形态学的聚类算法，该算法能够处理任意形状的聚类，采用启发式方法自动确定最优聚类数。同时，该算法也可以在矢量型空间数据库中得到实现。试验表明算法是可行和有效的，且能处理存在噪音的数据。

Cluster analysis is an important technique for data mining and knowledge discovery in spatial databases. Its main advantage is the ability to find interesting structures or clusters directly form the spatial data without using any background knowledge. Some available algorithms are reviewed and a mathematical morphology based clustering algorithm (MMC) is presented in this paper. Clusters with arbitrary shape can be discovered by using MMC algorithm, and the optimal cluster number is automatically determined by a heuristic method. The algorithm can be implemented in vector databases as well as in raster databases. The experiments show that the new algorithm is feasible and effective for discovering clusters in spatial databases and is robust when clustering in databases with noise.