Constructing Customer Consumption Classification Models Based on Rough Sets and Neural Network
Cao Xiaopeng · Industrial Engineering and Engineering Management · 2011
The customer consumption classification topic is receiving increasing attention from researchers in the field of customer relationship management.The current research on customer consumption classification can be further improved in many areas.For instance,customer consumption classification models should take into consideration multidimensional and other related consumption attributes into classification analysis,avoidance of attribute redundancy,and selection of core classification attributes.Customer consumption models should identify input neurons,hidden layers and hidden neurons in order to reduce the complexity of classification structure and improve model's explanatory power.Existing classification methods are not effective at representing the inconsistency of consumption attributes and classes.JPThis paper proposed a customer consumption classification model by integrating rough set and neural networks based on the rough set-neural network(RS-NN) model.Rough set is the core theory underpinning this study.This paper reduced attribute values and adopted core consumption attributes in order to solve attribute redundancy and inconsistency problems.This paper also used customer classification rules and solved attribute inconsistency problems.In addition,by integrating classification rules into neural networks this paper constructed a classification and parameters to reduce the complexity of the existing consumption classification model and training time,and improve a user's learning,reasoning and classification abilities.This paper adopted Rosetta V1.4.41 and MATLAB to construct a customer consumption classification model.This proposed model includes customer knowledge reduction pre-process,classification network construction and classification application.In the pre-process of customer knowledge reduction,we conducted an unsupervised discretization method to process continuous consumption attributes.This method enabled us to process qualitative data in isometric conversion method,form consumption sheet and produce discernibility matrix of logical operations.We were also able to reduce attribute values,obtain core attributes,and extract reduced knowledge space via rough classification rules and credibility.In the consumption classification model,we assigned input neurons,output neurons,hidden layers and hidden neurons,identified the relationships between nodes and established the weight of attributes in order to construct the initial topology of the RS-NN model.We then normalized two data sets with training samples representing 80% of the population and 20% of test samples.This typology enabled us to run the model with BP algorithm.We adopted the Tansig function as transfer function,calculated the errors with generalized δ rule,and tested the precision of the model.In the consumption application system,we classified new customers with RS-NN model,and produced a prediction report for customer consumption behaviors.The paper shows that a RS-NN model is better than a BP-NN algorithm in customer structure,model efficiency and classification prediction accuracy.The RS-NN model is an effective and practical method for customer classification.