Web Mining

Paper Code: 
25GBCA201A
Credits: 
03
Periods/week: 
03
Max. Marks: 
100.00
Objective: 

Course Objectives:

The course will enable the students to

1  Acquaint with the applications of web mining.

2  Elaborate different types of web data and web mining methods

 

Course Outcomes: 

Course

Learning Outcome

(at course level)

Learning and teaching

strategies

Assessment

Strategies

Course

Code

Course

Title

25GBCA201A

Web Mining (Theory)

CO79.  Analyse  the significance of data mining techniques  in  real-world applications.

CO80.Categorise   various approaches  to  web  data mining  and  explore  their practical applications.

CO81. Analyse  the structure  of data on web.

CO82. Discover patterns in web data using pattern recognition techniques.

CO83. Apply web mining techniques on various domains.

CO84. Contribute effectively in course-specific interaction.

Approach in teaching:

Interactive Lectures,Discussion, Reading Assignments Demonstrations.

Learning activities for the students: Self-learning assignments, Seminar presentation.

Class     test, SemesterEnd examinations, Quiz, Assignments, Presentation, Individual and groupprojects

 

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-Based Similarity 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: 

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

 

REFERENCES: 

SUGGESTED READINGS:

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

e –RESOURCES:

1.  https://youtu.be/9KFPB2LRnf4?list=PLaQ4ExxoPsDZj96MvExi7-ULeK1xoOaf_

2.  https://youtu.be/huhl1JZMW48?list=PLaQ4ExxoPsDZj96MvExi7-ULeK1xoOaf_

3.  https://slideplayer.com/slide/8189973/

4.https://www.academia.edu/32208992/Web_Mining_Accomplishments_and_Future     _Direc tions?auto=download

JOURNALS:

1. International Journal of Mining Science and Technology, Elsevier

 

Academic Year: 
2025-26
  • Printer-friendly version