WEB MINING

Paper Code: 
GBCA 201A
Credits: 
3
Periods/week: 
3
Max. Marks: 
100.00
Objective: 

The course will enable the students to

  1. Acquaint students with the applications of web mining.
  2. Elaborate with different types of web data and web mining methods

Course Outcomes (COs):

Course Outcome (at course level)

Learning and teaching strategies

Assessment Strategies

The students will:
CO67. Identify key concepts of Web mining to discover useful information from the World-Wide Web and its usage patterns
CO68. Compare various methods of web data mining and its applications
CO69. Analyse the structure of data on web.
CO70. Develop skills of using WEKA tool to perform preprocessing, clustering, classification on web data.
CO71. Demonstrate various pattern discovery and analysis techniques
 

Interactive Lectures, Discussion,Tutorials, reading assignments, Demonstrations, G-suite. Self-learning assignments, Effective questions, Simulation, Seminar presentation

 

Class test, Semester end examinations, Quiz, Solving problems in tutorials, Assignments, Presentation, Individual and group projects

 

9.00
Unit I: 
Data Mining and Knowledge Discovery
Data mining and knowledge discovery, The KDD process, Data preparation for knowledge discovery, Introduction of various data mining techniques (Clustering, Classification, and Association rule mining), Supervised, semi supervised and unsupervised learning,
 
9.00
Unit II: 
Web Mining Process and Techniques
WWW, Web Mining, Web mining and Data mining, Types of web mining (Content, Usage and Structure), Types of data: Structured and Unstructured Data, Sources of Data, Stages of web mining (Preprocessing, Pattern discovery and analysis), Privacy Tradeoff
9.00
Unit III: 
Web Structure Mining
Web Link Mining, Hyperlink based Ranking, Page Rank, Link-BasedSimilarity Search - Enhanced Techniques for Page Ranking, Implementation Issues, Web Crawlers
9.00
Unit IV: 
Web Usage Mining
Click stream Analysis, Web Server Log Files, Pattern discovery and pattern analysis techniques (Session and Visitor Analysis, Cluster Analysis (K means clustering) and Visitor Segmentation, Association and Correlation Analysis, Analysis of Sequential Patterns, Classification and Prediction based on Web (Decision tree))
9.00
Unit V: 
Web Mining Applications
Personalized Customer Experience in B2C E-commerce, Web Search, Web wide user tracking, Auction Sites, Information Retrieval systems, Targeted marketing.
ESSENTIAL READINGS: 
  1. Bing Liu, “Web Data Mining Exploring Hyperlinks, Contents and Usage Data”, 2nd Edition, Springer New York, 2011.
  2. Gordon S. Linoff,Michael J.A. Berry, Mining the Web: Transforming Customer Data Into Customer Value, John Wiley & Sons
  3. Zdravko Markov, Daniel T. Larose, “Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage”, Wiley & Sons, 2007
  4. Jiawei Han,Michaline Kamber and Jian Pei, “Data mining concepts and techniques”, 3rd Edition,Morgan Kaufmann,2012

SUGGESTED READINGS:

  1. Soumen Chakrabarti, “ Mining the Web”, Morgan Kaufmann,2002

 

 

REFERENCES: 
JOURNALS:
  • International Journal of Mining Science and Technology, Elsevier
 
E-RESOURCES:
  • https://youtu.be/9KFPB2LRnf4?list=PLaQ4ExxoPsDZj96MvExi7-ULeK1xoOaf_
  • https://youtu.be/huhl1JZMW48?list=PLaQ4ExxoPsDZj96MvExi7-ULeK1xoOaf_
  • https://slideplayer.com/slide/8189973/
  • https://www.academia.edu/32208992/Web_Mining_Accomplishments_and_Future_Directions?auto=download
 
Academic Year: 
2023-24
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