Discovery of Customer Behavior with Data Mining in E-Commerce


  • J. Umamaheswari
  • R. Gokulakrishnan


Data Mining is one of the basic techniques of extracting the required data from the huge collection of datasets produced in the various fields. This involves business area too whereas the significance of data mining benefit in business application is by discovering associative data and knowledge that can be utilized to produce good decision making over business area. However, the current e-commerce has been developing firmly which produces the service and resources over internet consistently. Simultaneously, the circumstance of fraudulent proceeding with users has reached the e-commerce system. In this period, e-commerce is deliberated as a killer-domain to succeeded mining data that provideappropriate factors from case to case but the most traditional method which e-commerce is able to process is Customer Relationship Management (CRM). Hence, this paper discusses about the data analysis that deals with the design of predicting customer behavior and relationship to manage the e-commerce business in a systematic manner. This proposed model has integrated K-Means clustering technique and classification using Neural Network (NN) as the data mining techniques for dealing both attributes of discrete and continuous to extract the weight of 5 major factors with customer credit card datasets for hidden knowledge. Thus, it efficiently supports in discovering the actual customer behavior and even classify recent customer in future with less time to improve CRM recommendation to customer in improving e-commerce business prediction performance.