Study and Evaluation of user's behavior in e-commerce Using Data Mining
Belsare Satish, Patil Sunil · 2012
Data mining has matured as a field of basic and applied research in computer science. The objective of this dissertation is to evaluate, propose and improve the use of some of the recent approaches, architectures and Web mining techniques (collecting personal information from customers) are the means of utilizing data mining methods to induce and extract useful information from Web information and service where data mining has been applied in the fields of e-commerce and e-business (that means User’s behavior). In the context of web mining, clustering could be used to cluster similar click-streams to determine learning behaviors in the case of e-learning or general site access behaviors in e-commerce. Most of the algorithms presented in the literature to deal with clustering web sessions treat sessions as sets of visited pages within a time period and do not consider the sequence of the click-stream visitation. This has a significant consequence when comparing similarities between web sessions. Wang and Zaiane propose an algorithm based on sequence alignment to measure similarities between web sessions where sessions are chronologically ordered sequences of page accesses.