Background: A new matter that arises in term of the dynamic behavior of customers is considering the customer segmentation. Based on the banks competitions to increase market share as well as the psychological and environmental factors the dynamics of customers’ b More
Background: A new matter that arises in term of the dynamic behavior of customers is considering the customer segmentation. Based on the banks competitions to increase market share as well as the psychological and environmental factors the dynamics of customers’ behavior should be considered over time. Transferring customers to different sectors over time and discovering the dominant models in their displacements between sectors are of important topics in this context. Objective: this article aims to identify the behavioral clusters, the dominant patterns of displacement, and the leading characteristics and patterns of customer displacements with a focus on the customer dynamics behavior of Ansar banks. Design/Methodology: A Hybrid method based on clustering and association rules has been proposed. Finding: four different behavioral group of customer are identified:" low-value customers with sustainable model", "low-value customer with unsustainable profitability model", "turned away customers with average profitability", "loyal customers with low profitability". Relations between of these groups are analyzed by association rules
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Knowledge extraction is one of the most significant problems of data mining. The principles raised in if-then format can be turned into real numbers in each section- as values which could be included in dataset. The suggested method in the present dissertation is applic More
Knowledge extraction is one of the most significant problems of data mining. The principles raised in if-then format can be turned into real numbers in each section- as values which could be included in dataset. The suggested method in the present dissertation is application of decision tree algorithms, clustering and forum rules for extraction of final rules. In the suggested method, extraction of rules is defined as an optimization problem and objective was obtaining a rule of high confidence, generalization and understandability. The suggested algorithm for extraction of rules was obtained from and tested based on a dataset of educational failure of 256 art school students living in Zanjan. The results suggested that the j48 algorithm in decision tree and accuracy of 0.95 is the choice for the dataset of educational failure. Data clustering was done by K-Main algorithm with confidence coefficient of 0.95. After all, obtaining rules of high confidence coefficient was done based on forum rules from Apriori algorithm for the whole datasets. The results of present study could be used for inhibition of educational failure of students, improved quality of relationship of parents and authorities with students and enhancing the education they receive.
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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 More
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.
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