Course code: PEC-CSE-405G
Category: Professional Elective Course
Course title: Web Mining
Scheme and Credits
| L | T | P | Credits | |
|---|---|---|---|---|
| Semester 7 | 3 | 0 | 0 | 3 |
| Marks | |
|---|---|
| Class work | 25 |
| Exam | 75 |
| Total | 100 |
| Duration of Exam | 03 Hours |
Objective of the course:
- To understand the architecture of web, mining the data, issues, challenges.
- To study the methods of extracting knowledge from web data, text and unusual data.
- To understand and use data mining language like R, Python etc.
- To understand the optimization of web and its applications.
Note: Examiner will set nine questions in total. Question one will be compulsory. Question one will have 6 parts of 2.5 marks each from all units and remaining eight questions of 15 marks each to be set by taking two questions from each unit. The students have to attempt five questions in total, first being compulsory and selecting one from each unit.
UNIT 1
Data Mining Foundations
Basic concepts in data Mining, Web mining versus Data mining, Discovering knowledge from Hypertext data.
An Overview of Web Mining
What is Web mining, Web mining taxonomy, Web mining subtasks, issues, challenges.
UNIT 2
Web Search and Information Retrieval
Information Retrieval Models, Web Search and IR, Text Mining, Latent Semantic Indexing, Web Spamming, Clustering and Classification of Web Pages, Information Extraction, Web Content Mining.
UNIT 3
Optimization
Introduction to Models and Concept of Computational Intelligence, Social Behavior as Optimization: Discrete and Continuous Optimization Problems, Classification of Optimization Algorithms, Evolutionary Computation Theory and Paradigm, Swarm and Collective intelligence.
UNIT 4
Swarm Intelligence Techniques
Particle Swarm Optimization, Ant Colony Optimization, Artificial Bees and Firefly Algorithm etc., Hybridization and Comparisons of Swarm Techniques, Application of Swarm Techniques in Different Domains and Real World Problems.
Suggested Text Books
- Witton Frank, Data Mining, Morgan Kauffan Publishers.
- Kennedy, J. and Eberhart, R.C., Swarm Intelligence, Morgan Kaufmann Publishers, 2001
- Bonabeau, E., Dorigo, M. and Theraulaz, G., Swarm Intelligence: From Natural to Artifical Systems, Oxford University Press, 1999
- Dorigo, M., Stutzle, T., Ant Colony Optimization, MIT Press, 2004
Suggested Reference Books
- Parsopoulos, K.E., Vrahatis, M.N., Particle Swarm Optimization and Intelligence: Advances and Applications, Information Science Reference, IGI Global, 2010
- Clerc, M., Particle Swarm Optimization, ISTE, 2006
- Nature Inspired Metaheuristic Algorithms, Xin-She Yang, Luniver Press, 2010
Course Outcomes
- Learn how the Web mining helps to improve the power of web search engine by classifying the web documents and identifying the web pages.
- How to predict user behaviour in the web.
- For a given data set how the optimization will be performed.