Detection of Knowledge Governing on Demographic Characteristics of Customers in Selecting Banks by through using Associative Rules in Data Mining

The purpose of the present study is to explore the dominant knowledge of the demographic characteristics of customers in choosing banks through  using associative rules in data mining. Effective decision-making and learning in a growing and complex world with with the help of  thinkers and executives is a necessary which also need employing some mechanisms to understand the structures of complex systems and mass data acquisition as well as  knowledge generation to make decisions. Most businesses identify their key customers through a variety of demographic characteristics. Businesses also target their consumers by promoting similar marketing features. Targeting consumers with similar demographic characteristics is useful for maximizing sales and profitability of the business. Banks are no exception to this rule  because they are essential elements of the economy of a country. Data mining solves this problem through  providing methods and software for automating analytics and discovering large and complex data sets. This research was conducted according to CRISP-DM standard and data were collected by questionnaire. Then, the results were converted into a database of ninety sources and after that they were extracted by using SPSS modeler software association rules for each bank. Extraction rules show how changing variables have an effect on other factors and ultimately on achieving goals.