Spatial clustering is an important tool for spatial data mining and spatial analysis.It can be used to discover the spatial association rules and spatial outliers in spatial datasets.Currently most spatial clustering algorithms cannot obtain satisfied clustering results in the case that the spatial entities distribute in different densities

and therefore more input parameters are required.To overcome these limitations

a novel data field for spatial clustering

called aggregation field

is first of all developed in this paper.Then a novel concept of aggregation force is utilized to measure the degree of aggregation among the entities.Further

a field-theory based spatial clustering algorithm（FTSC in abbreviation） is proposed.This algorithm does not involve the setting of input parameters

and a series of iterative strategies are implemented to obtain different clusters according to various spatial distributions.Indeed

the FTSC algorithm can adapt to the change of local densities among spatial entities.Finally

two experiments are designed to illustrate the advantages of the FTSC algorithm.The practical experiment indicates that FTSC algorithm can effectively discover local aggregation patterns.The comparative experiment is made to further demonstrate the FTSC algorithm superior than classic DBSCAN algorithm.The results of the two experiments show that the FTSC algorithm is very robust and suitable to discover the clusters with different shapes.