User Behavior Analysis And Mining Based On Web Log

With the emergence of Web 2.0 era, the Internet has become such an important channel for people to obtain all sorts of information and resources throughout the world. Agencies of business, enterprise, government and education are accelerating the accumulation of vast amounts of data stored on it. Internet has become one of the richest and most dense places for information storage and exchange.Faced with such a valuable resource, how to obtain, analyze and mining the knowledge and user behavior has become one of the critical requirements for Internet Company.The proposal of Web data mining is to bring about an effective solution addressing the above-mentioned requirement. Aiming at analyzing web logs and user behavior, it can identify and discover valuable knowledge and patterns using data mining techniques.This thesis has conducted in-depth study on the whole procedure of data mining beginning from web log mining to user behavior analysis. The main research work is stated as below:1) This thesis presents the concept and classification of Web data preprocessing techniques, especially in data cleaning, session identification, transaction recognition and etc. It also explores the interrelations between queries and web pages, calculating the similarity values between them and proposes an improved similarity algorithm to boost the accuracy of the algorithm.2) Implementing maximum forward access path (MFP) methods and clustering to identify user behaviors. The time factor is also introduced into the traditional model to reveal the user interest over time.The experimental results show that all the approaches proposed can effectively improve the analysis of user intent and mining user behavior. Compared with traditional techniques, the accuracy and efficiency are much improved. In addition, these approaches can also be applied directly or partly to other various online resources. Thus, the results of this study have a general significance in practice.