Web Mining Lab

Paper Code: 
25GBCA201B
Credits: 
03
Periods/week: 
06
Max. Marks: 
100.00
Objective: 

Course Objectives:

The course will enable the students to

1  Acquaint students with web mining through WEKA tool.

2  Analyse data with different web mining methods.

 

Course Outcomes: 

Course

Learning Outcome

(at course level)

Learning and

teaching strategies

Assessment

Strategies

Course

Code

Course

Title

25GBCA201B

Web Mining Lab

(Practical)

CO85.Identify the need of data warehouse in web mining.

CO86. Perform installation of WEKA tool to inspect data.

CO87. Create and access datasets to implement mining.

CO88.    Compare    the outputs     of     different models  and  select  the best     as     per     the application requirement

CO89. Apply  mining algorithms on datasets to analyse and visualize the results.

CO90. Contribute effectively in course- specific interaction.

Approach in teaching:

Interactive Lectures,Discussion, Tutorials, reading assignments, Demonstrations, G- suite.

Learning activities  for   the students:

Self-learning assignments,Simulation,   Seminar presentation,   giving tasks, Performing practical

Class  test, Semester end      examinations,Quiz,      Assignments,Presentation,Individual  and  group projects

 

Unit I: 

This course will be based on theory 24GBCA201B.  Exercises given will be covering entire syllabi as follows:

1. Data Warehouse: introduction and its significance.

2. Installation of WEKA tool

3. Preparation of training data

4. Data files supported by WEKA

5. Data types

6. Exercises related to Preprocessing

7. Exercises related to Classification

8. Exercises related to Clustering

 

REFERENCES: 

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_     Directions?auto=download

JOURNALS:

1.  International Journal of Mining Science and Technology, Elsevier

 

Academic Year: