Amalgamation of Web Usage Mining and Web Structure Mining

Web Mining can be classified into three main areas: Web Usage Mining, Web content Mining and Web Structure Mining. Web usage mining is a kind of web mining, which exploits data mining techniques to discover valuable information from navigation behavior of World Wide Web users. There are generally three tasks in Web Usage Mining: Preprocessing, Pattern analysis and Knowledge discovery. Preprocessing cleans log file of server by removing log entries such as error or failure and repeated request for the same URL from the same host etc... The main task of Pattern analysis is to filter uninteresting information and to visualize and interpret the interesting pattern to users. The statistics collected from the log file can help to discover the knowledge. This knowledge collected can be used to take decision on various factors like Excellent, Medium, Weak users and Excellent, Medium and Weak web pages based on hit counts of the web page in the web site. The topology of the website is restructured based on user’s behavior or hit counts which provides quick response to the web users, saves memory space of servers and thus reducing HTTP requests and bandwidth utilization. This paper addresses challenges in three phases of Web Usage mining along with Web Structure Mining.

[1]  Myra Spiliopoulou,et al.  Web usage mining for Web site evaluation , 2000, CACM.

[2]  Jaideep Srivastava,et al.  Web usage mining: discovery and applications of usage patterns from Web data , 2000, SKDD.

[3]  Georgios Paliouras,et al.  Web Usage Mining as a Tool for Personalization: A Survey , 2003, User Modeling and User-Adapted Interaction.

[4]  Enrique Frías-Martínez,et al.  A Customizable Behavior Model for Temporal Prediction of Web User Sequences , 2002, WEBKDD.

[5]  Tao Luo,et al.  Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization , 2004, Data Mining and Knowledge Discovery.

[6]  Olfa Nasraoui,et al.  An Evolutionary Approach to Mining Robust Multi-Resolution Web Profiles and Context Sensitive URL Associations , 2002, Int. J. Comput. Intell. Appl..

[7]  Jaideep Srivastava,et al.  Automatic personalization based on Web usage mining , 2000, CACM.

[8]  Michael D. Smith,et al.  Using Path Profiles to Predict HTTP Requests , 1998, Comput. Networks.

[9]  Peter Pirolli,et al.  Mining Longest Repeating Subsequences to Predict World Wide Web Surfing , 1999, USENIX Symposium on Internet Technologies and Systems.

[10]  M. Tamer Özsu,et al.  A Web page prediction model based on click-stream tree representation of user behavior , 2003, KDD '03.

[11]  Ramakrishnan Srikant,et al.  Mining web logs to improve website organization , 2001, WWW '01.

[12]  Ben Shneiderman,et al.  Designing the User Interface: Strategies for Effective Human-Computer Interaction , 1998 .

[13]  Craig S. Miller,et al.  Modeling Information Navigation: Implications for Information Architecture , 2004, Hum. Comput. Interact..

[14]  Yannis Manolopoulos,et al.  . EFFECTIVE PREDICTION OF WEB-USER ACCESSES: A DATA MINING APPROACH , 2001 .

[15]  Mary Czerwinski,et al.  Web page design: implications of memory, structure and scent for information retrieval , 1998, CHI.

[16]  Bing Liu,et al.  Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data , 2006, Data-Centric Systems and Applications.