Mohammad’reza Gholamian; Azimeh Mozafari
Abstract
Management and evaluation of valuable customers, is one of the most important banking factors to reduce costs and increase profitability. In recent decades, many researchers have studied on the analysis of the customer attributes to evaluate value of them using data mining techniques and decision tree ...
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Management and evaluation of valuable customers, is one of the most important banking factors to reduce costs and increase profitability. In recent decades, many researchers have studied on the analysis of the customer attributes to evaluate value of them using data mining techniques and decision tree is one of the most widely used data mining algorithms in the field. Since this algorithm for built tree, considers only one attribute at a time to test each node and ignores the dependency between attributes, therefore, required maximum memory is increased. To solve this problem, in this research a method is proposed to improve the decision tree using neural network to explore the dependency between the attributes based on reduction in required maximum memory that is used based on RFM model to predict customer values. Results show that the proposed method using dependencies between attributes will predict the new customer values by less maximum memory compare to the basic method
Jamshid salehisadegheiani; Samaneh Sorournejad; Reza Ebrahimi Atani; Maryam Akhavan Kharazian; Mousa Rezvani Chamazamin
Volume 1, Issue 2 , December 2013, , Pages 147-162
Abstract
In recent years, a number of new payment solutions have been introduced in mobile commerce although with less success. The existence of standardized and widely accepted mobile payment (also known as MP) procedures is crucial for successful business-to-customer mobile commerce. On the other hand, Non-acceptance ...
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In recent years, a number of new payment solutions have been introduced in mobile commerce although with less success. The existence of standardized and widely accepted mobile payment (also known as MP) procedures is crucial for successful business-to-customer mobile commerce. On the other hand, Non-acceptance of innovations has generally been attributed to the failure of laggards to keep up with the time. While our previous survey has investigated positive adoption decisions, this paper focuses on consumer resistance against innovation instead. In this paper we examine the conditions of resistance to the MP procedures by Iranian customers. In fact we attempts to discover causes that force an Iranian customer in particular to resist to innovation such as mobile payment. Based on the theory of innovation resistance and the mobile payment literature, we designed a questionnaire which is distributed amongst our understudy society of Ordinary Iranians. We employed data mining techniques in order to extract useful patterns from data, which describe the reasons of that refusal associated with different social and cultural levels of the society.
