The study on immune spatial clustering model based on obstacle

Spatial clustering methods are mainly to group spatial objects based on their characteristics such as distance, connectivity, or their relative density in space. In the real world, many physical obstacles exist such as rivers, lakes and highways, and their presence may affect the results of clustering substantially. The problem of clustering in the presence of obstacles was studied and defined. As a solution to this problem and based on K-medoids, a scalable new clustering algorithm, called immune spatial clustering model based on obstacle was proposed. Various forms of pre-processed information that could enhance the efficiency of immune spatial clustering model were discussed. Various test data show that immune spatial clustering model is both efficient and effective.