Web Usage Mining Using Discovered Frequent Pattern Algorithms: Basics, Steps, Rules, and comparison about algorithms of extracting frequent patterns

Developing word of the web and increasing the content information, web site user’s requirements has been changed. Therefore web needs a dynamic and accurate algorithm to recognize user’s requirements to suggest new patterns. Web mining helps to solve the problem of discovering how users are using Web sites. It involves mining logs (or log analysis) and the steps that typically have to be gone through to get meaningful data from Web logs - data collection, pre-processing, data enrichment and pattern analysis and discovery. This book presents methods, approaches and techniques to perform three main tasks of web usage mining (Preprocessing, Pattern discovery and Pattern analysis). In another part of book discussed techniques of WUM to design Web recommender systems and shown that how WUM can be applied to Web server logs for discovering access patterns. The important part of this book discussed about static and dynamic algorithms that build path tree and extract frequent pattern of web user’s. Finally the dynamic algorithm compared together such as CATS-tree, AFPIM, CAN-tree, CP-tree and I-FARM, In terms of time complexity and executed speed.