WEB MINING AND ANALYTICS

Paper Code: 
MCA 325D
Credits: 
04
Periods/week: 
04
Max. Marks: 
100.00
Objective: 

This course enables the students to

  1. Appraise the use of machine learning approaches for Web Content Mining
  2. Illustrate the various aspects of web usage mining
  3. Develop skills of using recent data mining software for solving practical problems of Web Mining
  4. Evaluate emergent features such as the structure and evolution of the Web graph, its traffic patterns, and the spread of information

 

Course Outcomes(COs):

Learning Outcome (at course level)

Learning and teaching strategies

Assessment Strategies

CLO133. Review basic concepts for web content mining. 

CLO134. Interpret Page Ranking algorithm and modify the algorithm for mining information 

CLO135. Prepare an existing search engine to make it personalized using web analytics

CLO136. Select the appropriate algorithm for extracting the information

Approach in teaching:

Interactive Lectures, Discussion,

Demonstration, Experiment

 

Learning activities for the students:

Self-learning assignments, Quiz activity, presentation, flip classroom,  

  • Assignments
  • Written test in classroom
  • Classroom activity
  • Continuous Assessment
  • Semester End Examination

 

12.00
Unit I: 
Introduction

Introduction – Web Mining – Theoretical background –Algorithms and techniques –

Association rule mining – Sequential Pattern Mining -Information retrieval and Web search – Information retrieval Models-Relevance Feedback- Text and Web page Pre-processing

 

14.00
Unit II: 
Web Content Mining

Web Content Mining – Supervised Learning – Decision tree - Naive Bayesian Text

Classification -Support Vector Machines - Ensemble of Classifiers. Unsupervised Learning - K-means Clustering -Hierarchical Clustering –Partially Supervised Learning

 

14.00
Unit III: 
Web Structure and Web Usage Mining

Hyperlink based Ranking – Introduction -Social Networks Analysis- Co-Citation and Bibliographic Coupling - Page Rank -Authorities -Enhanced Techniques for Page Ranking - Community Discovery – Web Crawling -A Basic Crawler Algorithm- Implementation Issues

Web Usage Mining – sources of data- Applications -Click stream Analysis -Web Server Log Files - Data Collection and Pre Processing- Cleaning and Filtering- Data Modeling for Web Usage Mining – Issues- Discovery and Analysis of Web Usage Patterns – Used tools in Web Usage mining.

 

10.00
Unit IV: 
Introduction to web analytics

Motivation and historical perspective on the development of web analytics, Display and search advertising , Knowledge discovery from web data, Major computing paradigms, Typical problem formulations

 

10.00
Unit V: 
Web analytics at e-Business scale

Framework for mapping business needs to web analytics tasks, Data collection architecture, Introduction to OLAP, Web data exploration and reporting, Introduction to Splunk

 

ESSENTIAL READINGS: 
  • Bing Liu, “ Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications)”, Springer; 2nd Edition 2009

 

REFERENCES: 
  • Guandong Xu ,Yanchun Zhang, Lin Li, “Web Mining and Social Networking: Techniques and Applications”, Springer; 1st Edition.2010
  • Zdravko Markov, Daniel T. Larose, “Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage”, John Wiley & Sons, Inc., 2007

 

Academic Year: 
2021-22
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