Siavash Emtiyaz - Department of Computer Engineering, Islamic Azad University of Sardasht, Sardasht, Iran
Shilan RahmaniAzar - Department of Computer Engineering, Islamic Azad University of Uromia, Uromia, IranAbstract

The most significant and impressing element in business, is the customer factor, that right after the entrance of the customer. The circuit of information stat to run the other subsequent progress in an organization. The mass amount entered information by customers, crested huge databases which are belonged to them. Now we are required to adopt the appropriate and props managing methods, to control and manage the data, in order to imp are the organization procedure and increase the resulted effectiveness. Data Mining is one of the suggested solutions for this issue, which almost is the best among all, and prepares the results with the least interruptions from the customer or user automatically explores the logical relationships algorithms. The main problem related to the interactions and relationships with customer is originated from the labeling, the majority of user's in formations are not labeled, although a few of them labeled learning the semi-supervised, is considered as a new reaction in the field of machine learning, that provides a good result with a few labeled information, accurately.The aim of this research is to modeling the attitude of customers via the semi-supervised method of management. One of the main requirements in the relationships with the customers in marketing is to create more complicates and effective model of the customer's attitudes, and also developing them. Hence, probably due to its difficulty it is considered as one of the problems in the customer's management. Accordingly for the time being to solve this problem and consenting the limitation of excited labeled data, the usage of the data mining based on the semi-supervised learning can be beneficial in order to accomplish a good method in the procedure of managing the relationships with the customers, to classify the significant users

Customer Relationship Management, Data Mining, Semi-Supervised Learning, self-training algorithms