Application and study of spatial cluster and customer partitioning

Along with the development of database technology and information collection methods, spatial data mining has become more and more important, and presents new challenges that are for the large size of spatial data and complexity of spatial data types. This paper introduces the research of current spatial cluster algorithms, and we propose a new and efficient algorithm based on the theory of partitioning methods, grid-based methods and density methods. This algorithm can find arbitrarily-shape clusters without any previous knowledge, and scale well for large data sets due to its computational complexity not to connect with the number of objects. This paper employs spatial cluster in the customer partitioning of business management so as to solve spatial analysis and location.