A Comparative Analysis Study of Data Mining Algorithm in Customer Relationship Management

With the rapid development of database technology and the wide application of database management system, an increasing number of data are accumulated in the database or data warehouse of enterprises. As the most important resource of enterprises, customers leave a great deal of information in the operation of enterprises. The entrepreneurs manage the customer relationship and analyze customers’ materials by means of informatization in order to verify the customers’ needs more accurately and offer correct decision-making schemes for the enterprises’ further marketing and development. This thesis analyzes the classification algorithm, the most representative algorithm in data mining algorithms at present, which includes decision tree algorithm, Bayesian classification, neural network classification algorithm, rough set classification algorithm. It summarizes the property and characteristic of each algorithm by analyzing and comparing the theory of the present representative classification algorithms in the following aspects: the accuracy rate (the correct, predictive and unseen class label capacity of data of classification models), the speed (the computational costs of producing and using models, i.e., time complexity), the robustness (the model’s ability to predict correctly for the given data with noise and missing value) and scalability (the valid ability of configurable model for the given data set with different numbers).