Abstract: Spatial data mining (SDM) is a branch of data mining (DM). Spatial clustering is an important research topic in SDM. This paper proposes a novel genetic K-means algorithm for spatial clustering after analyzing advantages and disadvantages of genetic algorithm and K-means algorithm. The spatial clustering algorithm can give attention to local constringency and the whole constringency. The experimental results show that it is better than simple genetic algorithm and tradition K-means algorithm.

Key words: Spatial data mining; Spatial clustering; Genetic algorithm; K-means algorithm；Genetic K-means algorithm

摘要： 空间数据挖掘是数据挖掘的一个新的分支，空间聚类分析是空间数据挖掘中的一个重要研究课题。本文在分析遗传算法及K–均值算法的优越性和不足的基础上，设计了一种遗传K-均值空间聚类分析算法，该算法兼顾了局部收敛和全局收敛性能。实验表明，其结果优于传统K-均值聚类方法及单纯的遗传算法聚类。

关键词: 空间数据挖掘；空间聚类；遗传算法；K-均值算法；遗传K-均值算法