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Evolving dynamic web pages using web mining


Evolving dynamic web pages using web mining Kartik Menon Smart Engineering Systems Laboratory Engineering Management Department University of Missouri-Rolla – PowerPoint PPT presentation

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Title: Evolving dynamic web pages using web mining

Evolving dynamic web pages using web mining
Kartik Menon Smart Engineering Systems
Laboratory Engineering Management
Department University of Missouri-Rolla
  • Goal
  • Web Mining
  • General Principle behind web mining
  • Web Data
  • Web Access Pattern Clustering
  • Evolving web pages using cluster information
  • Clustering Techniques
  • Fuzzy C means
  • Experimental Set-up
  • Results
  • Conclusion and Future work
  • Questions

  • Cluster similar web access traversal patterns
    and train the system to understand the needs and
    demands of different users accessing the website
    and use this information to evolve web pages.

Web Mining
  • Web Mining
  • Learning about different users accessing a web
  • The needs and requirements of the user
  • Web Access Traversal Patterns
  • Links which are more popular than others
  • For example www.yahoo.com
  • Emails
  • Search engine
  • News
  • Greeting cards

General Principle behind web mining
  • Gather web data from Web Log servers
  • Cluster web traversal patterns
  • Evolve web pages

Web Data
  • What information is important for Mining
  • Links traversed (URLs requested)
  • Documents downloaded
  • Time spent on the web page as compared total time
  • Web Traffic
  • GET or POST messages

Web Access Pattern Clustering
  • Find users with similar web access patterns
  • Grouping and separating users
  • Concise representation of a system's behavior
  • Generalize about user needs and interests

Evolving Web Pagesusing cluster information
  • The cluster information can be used
  • To know about users
  • Modify the web page
  • Web personalization
  • Evolving Web pages

Clustering Techniques
  • Neural Nets
  • Kohonens Self Organizing Maps (SOMs)
  • Statistical
  • K-Means
  • Fuzzy Logic
  • Fuzzy C Means
  • Fuzzy ISODATA

Fuzzy C Means
  • Is a data clustering technique where each data
    point belongs to a cluster to some degree that is
    specified by a membership function
  • If
  • X is a set of n data sample vectors
  • U is a partition of X in c part,
  • V are cluster centers
  • d2 is an inner product induced norm
  • u grade of membership of xk to the cluster i
    between 0 and 1
  • m is a parameter to increase or decrease the

Fuzzy C Means (contd)
Experimental Set-up
  • Target the website http//campus.umr.edu.
  • Mine the web log files for web data.
  • The main problem is to convert the web sites
    accessed into numeric values.
  • Identify all the URLs from where you can go from
    this web page
  • Number these URLs from 1 to N where N is the Nth
    URL which can be accessed
  • Assign fuzzy weights (w(j)) to each URL that can
    be accessed
  • A Boolean variable s(j) is defined which is set
    to 1 if the jth URL is accessed by the user else
    s(j) is set to null.

Experimental Set-up (contd.)
  • Define the data point x as the number
    corresponding to the for all the sites accessed
    by the user in that particular user session.
  • Apply fuzzy c-means by calculating Euclidean
    distance between the data sample as dijxj-ci
    where xj being the data point and ci being the
    center of cluster i.



IP Address URLs Accessed by the user http//campus.umr.edu, /students, /departments, /departments/academic.htmlarts_science http//campus.umr.edu, /students, /registrar, /registrar/star http//campus.umr.edu, /students, http//web.umr.edu/career, /jobtrak/ http//campus.umr.edu, /students, http//web.umr.edu/career, /fairs
Results For 2 and 3 clusters
Results For 2 and 3 clusters(contd)
Web Page Evolution
  • Use the clustered information as
  • an input to modify the web page so that
  • users having similar access patterns get same
    web page as compared to others
  • Adjust the placement of links
  • Remove certain links (if possible)

  • Fuzzy c-means is an easy way of
  • clustering similar web access patterns
  • for different user sessions
  • The use of Euclidean distance was very helpful to
    learn more about these web access patterns.
  • The experiment provided easy results and plots
    which was highly interpretable
  • We observe that that fuzzy c-means provided
    stable results for the different data sets we

Future Work
  • Use other clustering algorithms
  • and compare
  • Developing self evolving web sites - sites that
    improve themselves by learning from user access
  • The results which we got using the fuzzy
    clustering algorithms could be used to recommend
    the web master of the http//campus.umr.edu
  • Increase the popularity of the web page by
    tailoring it more to the needs of the users
    accessing it

Questions ???
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