Extracting of association rules between urban features provides latent and considerable information for urban planners about the relationships between urban characteristics and their similarities. For this purpose, in this paper, the most famous and well-known Apriori algorithm is used. We present the Fariori algorithm to delay the characteristics that can be deleted during execution, as well as to achieve main and frequent features in the early stages with efficient changes to the Apriori algorithm. Although the spatial and temporal complexity of both algorithms is exponential based on the number of fea-tures, in practice, by implementing the Fariori algorithm in MATLAB, we achieved more rules than the existing software (R, Weka, Market Basket Analysis and, Yarpiz). In the proposed algorithm, it is possible to determine the degree of similarity by adjust-ing the support and confidence ratio parameters to identify a coherent set of similar cities. The used database includes cities of 31 in the provincial capitals of Iran. Dis-covering the association rules leads to similar cities finding and can be an efficient aid in the decision-making process.