Frequent pattern discovery is a heavily focused area in data mining. Discovering concealed information from Web log data is called Web usage mining. Web usage mining discovers interesting and frequent user access patterns from web logs. This paper contains a novel approach, based on k-mean and frequent pattern tree (FP-tree), for frequent pattern mining from Weblog data.

The paper summarizes the different characteristics of Web data, the basic components of Web mining and its different types, and the current state of the art. The reason for considering Web mining, a separate field from data mining, is explained. The limitations of some of the existing Web mining methods and tools are enunciated, and the significance of soft computing (comprising fuzzy logic (FL...

| The article summarizes the diierent characteristics of web data, the basic components of web mining and its diierent types, and their current states of the art. The reason for considering web mining, a separate eld from data mining, is explained. The limitations of some of the existing web mining methods and tools are enunciated, and the signiicance of soft computing (comprising fuzzy logic, ...

This chapter discusses web usage mining techniques that can be applied for building adaptive hypermedia systems. These techniques are used for uncovering hidden patterns within web access data and then for building the user model that lies in the heart of each adaptive system. Web access data, traditionally stored in the server log files, constitute a rich source of data collected in a non-intr...

Web mining is a one class of data mining. Web Mining is a variation of data mining that distills untapped source of abundantly available free textual information. The need and importance of web mining is growing along with the massive volumes of data generated in web day-to-day life. Web data Clustering is the organization of a collection of web documents into clusters based on similarity. A go...

Application of data mining techniques to the World Wide Web, referred to as Web mining, has been the focus of several recent research projects and papers. However, there is n o established vocabulary, leading to confusion when comparing research efforts. The t e r m W e b mining has been used in two distinct ways. T h e first, called Web content mining in this paper, is the process of informati...

Sequential pattern mining is the process of applying data mining techniques to a sequential database, to extract frequent subsequences to discover correlation that exists among the ordered list of events. Web Usage mining (WUM) discovers and extracts interesting knowledge/patterns from Web logs is one of the applications of Sequential Pattern Mining. In this paper, we present a survey of the se...

As the popularity of WWW explodes, a massive amount of data is gathered by Web servers in the form of Web access logs. This is a rich source of information for understanding Web user surfing behavior. Web Usage Mining, also known as Web Log Mining, is an application of data mining algorithms to Web access logs to find trends and regularities in Web users' traversal patterns. The results of Web ...

Web Usage Mining make use of Association Rule Mining to discover the interesting pattern, identify web user behavior, predict web user expectation and improve the business strategy. Association Rule Mining is a technique of Data Mining which is used to find the relationship between the data items. In Web Usage Mining, data are stored in the web server in the form of web log files. Numerous amou...