Clustering Web Access Patterns Using Fuzzy Clustering Algorithms

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Clustering Web Access Patterns Using Fuzzy
Clustering Algorithms

• Kartik Menon

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Overview

• Goal
• Web Mining
• Web Data
• Web Access Pattern Clustering
• Clustering Techniques
• Fuzzy C means
• Experimental Set-up
• Results
• Conclusion and Future work
• Questions

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Goal

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

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Web Mining

• Web Mining
• Learning about different users accessing a web
  page.
• 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

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Web Data

• What information is important for Mining
• Links traversed (URLs requested)
• Documents downloaded
• Time spent
• GET or POST messages
• Web Log servers

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Web Access Pattern Clustering

• Find users with similar web access patterns
• Grouping and separating users
• Concise representation of a system's behavior

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Clustering Techniques

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

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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
  fuzziness.

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Fuzzy C Means (contd.)
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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.

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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.

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Results - For 2 and 3 clusters
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Conclusions

• 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
  took.

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Future Work

• Use other clustering algorithms and compare
• Developing self evolving web sites - sites that
  improve themselves by learning from user access
  patterns
• 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

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Questions ???