Web usage mining: discovery and applications of usage patterns from Web data

  title={Web usage mining: discovery and applications of usage patterns from Web data},
  author={Jaideep Srivastava and Robert Cooley and Mukund Deshpande and Pang-Ning Tan},
  journal={SIGKDD Explor.},
Web usage mining is the application of data mining techniques to discover usage patterns from Web data, in order to understand and better serve the needs of Web-based applications. Web usage mining consists of three phases, namely preprocessing, pattern discovery, and pattern analysis. This paper describes each of these phases in detail. Given its application potential, Web usage mining has seen a rapid increase in interest, from both the research and practice communities. This paper provides a… 

Figures from this paper

Web Usage Mining: A Research Area in Web Mining

A study on web usage mining, its methods and applications, which focuses on the techniques that could predict user's behavior while the user interacts with web.

Web Usage Mining: Finding Usage Patterns from Web Logs

This paper gives an overview of web log file, describes its various types and gives a detail of the process of Web usage mining, and outlines future research directions in area of web usage mining.

A survey paper on techniques and applications of web usage mining

This paper focuses on the working of web usage mining, data sources forweb usage mining and applications of web use mining, which is an implementation part of data mining.

Web Miner: A Tool for Discovery of Usage Patterns From Web Data

This paper deals with the Web usage mining of a website which is hosted on IIS web server and the research is being performed on a log file using Log Parser.

Web Usage Mining Systems and Technologies

A survey and analysis of current Web usage mining systems and technologies is provided and a list of major research systems and projects concerning Web usagemining is presented.

Web usage mining: Discovery of the users' navigational patterns using SOM

Kohonen's SOM (Self Organizing Map) is applied to pre-processed web logs of the authors' university web server logs and extracted frequent patterns and would be useful for the university web site owner.

A Short Survey of Web Data Mining

This paper gives a short description of each web mining category and describes subcategories with examples of the methods used to mine each subcategory.

An Overview on Web Usage Mining

This paper presents each phase in detail, the process of extracting useful information from server log files and some of application areas of Web Usage Mining such as Education, Health, Human-computer interaction, and Social media.

Web Usage Mining: users' navigational patterns extraction from web logs using ant-based clustering method

Ant-based clustering is applied to pre-processed logs of the authors' university web server logs to extract frequent patterns for pattern discovery and then it is displayed in an interpretable format.

A Critique on Web Usage Mining

Some of the existing web usage mining techniques are given, which are crucial for network traffic flow analysis, creating business services, business support, etc.



Web mining: information and pattern discovery on the World Wide Web

This paper defines Web mining and presents an overview of the various research issues, techniques, and development efforts, and briefly describes WEBMINER, a system for Web usage mining, and concludes the paper by listing research issues.

Discovering Web access patterns and trends by applying OLAP and data mining technology on Web logs

  • Osmar R ZaianeM. XinJiawei Han
  • Computer Science
    Proceedings IEEE International Forum on Research and Technology Advances in Digital Libraries -ADL'98-
  • 1998
The design of WebLogMiner is presented, current progress is reported and future work in this direction is outlined, which can improve the system performance, enhance the quality and delivery of Internet information services to the end user, and identify populations of potential customers for electronic commerce.

Discovery of Interesting Usage Patterns from Web Data

A quantitative model based on support logic for determining the interestingness of discovered patterns is developed and incorporated into the Web Site Information Filter system and examples of interesting frequent itemsets automatically discovered from real Web data are presented.

Creating adaptive Web sites through usage-based clustering of URLs

An effective technique for capturing common user profiles based on association rule discovery and usage based clustering is proposed and techniques for combining this knowledge with the current status of an ongoing Web activity to perform real time personalization are proposed.

SpeedTracer: A Web Usage Mining and Analysis Tool

The design of SpeedTracer is described and some of its features are demonstrated with a few sample reports, helping the understanding of user surfing behavior.

Discovering Internet marketing intelligence through online analytical web usage mining

A novel way of combining data mining techniques on Internet data in order to discover actionable marketing intelligence in electronic commerce scenarios is described, which include marketing expertise as domain knowledge and are specifically designed for electronic commerce purposes.

Data Mining: An Overview from a Database Perspective

This article provides a survey, from a database researcher's point of view, on the data mining techniques developed recently, a classification of the available data mining Techniques, and a comparative study of such techniques is presented.

Data mining for path traversal patterns in a web environment

A new data mining capability which involved mining path traversal patterns in a distributed information providing environment like world-wide-web is explored, where the original sequence of log data is converted into a set of maximal forward references and filter out the effect of some backward references.

ParaSite: Mining Structural Information on the Web

Adaptive Web Sites: Conceptual Cluster Mining

This paper formalizes index page synthesis as a conceptual clustering problem and introduces a novel approach which is called conceptual cluster mining: a small number of cohesive clusters that correspond to concepts in a given concept description language L are searched for.