Abstract: At present,the research of multi-scale data mining mainly focuses on space image data,and recently has produced some results on the general data,including the multi-scale clustering and multi-scale association rules,but it has not been involved in the field of classification mining.Combining with fractal theory,this paper applied the theory,knowledge and methods related to the multi-scale data mining to the areas of the classification mining,and proposed an approach of similarity measure based on Hausdorff.Relative to the definition of weight through experience,this paper clearly defined it by the similarity of generalized fractal dimension to improve the precision of similarity measure.Then,this paper proposed a multi-scale classification scaling-up algorithm named MSCSUA(Multi-Scale Classification Scaling-Up Algorithm).At last,this paper performed experiments on four UCI benchmark data sets and one real data set (H province part of the population).The experimental results show that the thought of multi-scale classification is feasible and effective,the MSCSUA algorithm performs well in terms of classification than SLAD,KNN,Decision Tree and LIBSVM algorithms on different data sets.

Key words: Fractal theory, Multi-scale classification, Multi-scale data mining, Scaling-up