New Methods of Pruning Associative Classification Rules (Case Study: Rules Related to the Optimality of Location of Banks in Tehran City)

With the advance technology in discovery of data, data volume has increased. For this reason data mining methods, including Associative Classification for extracting knowledge from large data sources were used. At associative classification are used association rules for data classification. After classification rules generation, because large number of them, pruning methods are used to delete redundancy rules. In this research, the associative classification rules are used to determine relationship between the location of urban elements and optimality of location of bank branches and financial and credit institutions in Tehran. Because the location of the urban elements have a large impact on determining an optimal location for banks and optimal location of banks make greater profitability for them. After associative classification rules generation, four new method of pruning are being introduced to improve old ways that three methods reduce the number of rules and a method increases accuracy.