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Web Usage Pattern
SHAH RUSHABH R CE-111
SHREYANSH R KEJRIWAL CE-113
Outline
 Brief overview of Web mining
 Web usage mining
 Application areas of Web usage
mining
 Future research direct...
Web Mining
 Web Mining is the application of
data mining techniques to discover
and retrieve useful information and
patte...
Web Mining Categories
 Web Content Mining- extracting
knowledge from the content of the
Web
 Web Structure Mining- disco...
Web Usage Mining Processes
 Preprocessing: conversion of the raw data
into the data abstraction (users, sessions,
episode...
Web Usage Mining Processes
(Cont.)
Web Usage Mining- Preprocessing
 Data Cleaning: remove outliers and/or irrelative data
 User Identification: associate p...
Web Usage Mining -
Pattern Discovery Tasks
 Statistical Analysis: frequency analysis, mean,
median, etc.
◦ Improve system...
Web Usage Mining -
Pattern Discovery Tasks (Cont.)
 Classification: the technique to map a data
item into one of several ...
Web Usage Mining -
Pattern Discovery Tasks (Cont.)
 Sequential Patterns: extract frequently occurring
intersession patter...
Web Usage Mining -
Pattern Analysis
 Pattern Analysis is the final stage of WUM,
which involves the validation and
interp...
Web Usage Mining -
Pattern Analysis Methodologies and Tools
 Visualization: help people to understand both real and
abstr...
Application Areas for
Web Usage Mining
 Personalized: discover the preference and
needs ofindividual Web users in order t...
Future Research Directions
 Usage Mining on Semantic Web
◦ Help to build semantic Web
◦ With semantic Web, WUM can be
imp...
Future Research Directions
(Cont.)
 Analysis of Discovered Patterns
◦ Research on efficient, flexible and
powerful analys...
Conclusion
 Web usage and data mining to find patterns is a
growing area with the growth of Web-based
applications
 Appl...
THANK YOU
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Engineering pre-final/final semester project on data analtyics.

Very useful

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Web Usage Pattern

  1. 1. Web Usage Pattern SHAH RUSHABH R CE-111 SHREYANSH R KEJRIWAL CE-113
  2. 2. Outline  Brief overview of Web mining  Web usage mining  Application areas of Web usage mining  Future research directions
  3. 3. Web Mining  Web Mining is the application of data mining techniques to discover and retrieve useful information and patterns from the World Wide Web documents and services.
  4. 4. Web Mining Categories  Web Content Mining- extracting knowledge from the content of the Web  Web Structure Mining- discovering the model underlying the link structures of the Web  Web Usage Mining- discovering user’s navigation pattern and predicting user’s behavior
  5. 5. Web Usage Mining Processes  Preprocessing: conversion of the raw data into the data abstraction (users, sessions, episodes, clickstreams, and pageviews) necessary for further applying the data mining algorithm.  Pattern Discovery: is the key component of WUM, which converges the algorithms and techniques from data mining, machine learning, statistics and pattern recognition etc. research categories.  Pattern Analysis: Validation and interpretation of the mined patterns
  6. 6. Web Usage Mining Processes (Cont.)
  7. 7. Web Usage Mining- Preprocessing  Data Cleaning: remove outliers and/or irrelative data  User Identification: associate page references with different users  Session Identification: divide all pages accessed by a user into sessions  Path Completion: add important page access records that are missing in the access log due to browser and proxy server caching  Formatting: format the sessions according to the type of data mining to be accomplished.
  8. 8. Web Usage Mining - Pattern Discovery Tasks  Statistical Analysis: frequency analysis, mean, median, etc. ◦ Improve system performance ◦ Provide support for marketing decisions ◦ Simplify site modification task  Clustering: ◦ Clustering of users help to discover groups of users with similar navigation patterns => provide personalized Web content  ◦ Clustering of pages help to discover groups of pages having related content => search engine
  9. 9. Web Usage Mining - Pattern Discovery Tasks (Cont.)  Classification: the technique to map a data item into one of several predefined classes ◦ Develop profile of users belonging to a particular class or category  Association Rules: discover correlations among pages accessed together by a client ◦ Help the restructure of Web site ◦ Page prefetching ◦ Develop e-commerce marketing strategies
  10. 10. Web Usage Mining - Pattern Discovery Tasks (Cont.)  Sequential Patterns: extract frequently occurring intersession patterns such that the presence of a set of items followed by another item in time order ◦ Predict future user visit patterns=>placing ads or recommendations ◦ Page prefeteching  Dependency Modeling: determine if there are any significant dependencies among the variables in the Web domain ◦ Predict future Web resource consumption ◦ Develop business strategies to increase sales ◦ Improve navigational convenience of users
  11. 11. Web Usage Mining - Pattern Analysis  Pattern Analysis is the final stage of WUM, which involves the validation and interpretation of the mined pattern  Validation: to eliminate the irrelative rules or patterns and to extract the interesting rules or patterns from the output of the pattern discovery process  Interpretation: the output of mining algorithms is mainly in mathematic form and not suitable for direct human interpretations
  12. 12. Web Usage Mining - Pattern Analysis Methodologies and Tools  Visualization: help people to understand both real and abstract concepts ◦ WebViz: Web is visualized as a direct graph  Query mechanism: allow analysts to extract only relevant and useful patterns by specifying constraints. ◦ WEBMINER  On-Line Analytical Processing (OLAP): enable analysts to perform ad hoc analysis of data in multiple dimensions for decision-making ◦ WebLogMiner
  13. 13. Application Areas for Web Usage Mining  Personalized: discover the preference and needs ofindividual Web users in order to provide personalized Web site for certain types of users  Impersonalized: examine general user navigation patterns in order to understand how general users use the site ◦ System Improvement ◦ Site Modification ◦ Business Intelligence ◦ Web Characterization
  14. 14. Future Research Directions  Usage Mining on Semantic Web ◦ Help to build semantic Web ◦ With semantic Web, WUM can be improved  Multimedia Web Data Mining ◦ Representation, problem solving and learning from Multimedia data is indeed a challenge
  15. 15. Future Research Directions (Cont.)  Analysis of Discovered Patterns ◦ Research on efficient, flexible and powerful analysis tools  More Applications ◦ Temporal evolutions of usage behavior ◦ Improving Web services ◦ Detect credit card fraud ◦ Privacy issues
  16. 16. Conclusion  Web usage and data mining to find patterns is a growing area with the growth of Web-based applications  Application of web usage data can be used to better understand web usage, and apply this specific knowledge to better serve users  Web usage patterns and data mining can be the basis for a great deal of future research
  17. 17. THANK YOU

Engineering pre-final/final semester project on data analtyics. Very useful

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