Abstract: Data mining field has made some progress on the multi-scale research.However,the current research mostly focuses on the multi-scale mining of the space or image data.And traditional clustering mining has not separately stu-died the multi-scale characteristic of datasets.According to existing problems,this paper carried on the general study of multi-scale clustering mining theories and methods.Firstly,we extended scale definition on the basis of the concept hierar-chy and built multi-scale datasets.Secondly,we expounded the reasons and classification of scale conversion,meanwhile concluded the definition of the multi-scale clustering.Then,we introduced multi-scale clustering scaling up algorithm and multi-scale clustering scaling down algorithm based on the kriging theories.Finally,simulation experiments tested MSCSUA and MSCSDA with the help of public UCI clustering datasets and demographic dataset from H province.And the experimental results show that MSCSUA and MSCSDA are effective and feasible.

Key words: Multi-scale,Clustering,Scale conversion,Multi-scale clustering mining,Kriging