To operate the Blended Learning Center(BLC) at optimal level, maintenance will be performed every day at 8:30 AM and at 5:00 PM regularly which can take up to 30 minutes. Please consider scheduling your activity in the BLC platform accordingly.
Topic outline
- Welcome to Data Mining
Welcome to Data Mining
Class Recordings
CSE 450 K Class Recordings URL
Restricted Not available unless: You belong to Summer'21_Section K
CSE 450 M Class Recordings URL
Restricted Not available unless: You belong to Summer'21_Section M
CSE 450 L Class Recordings URL
Restricted Not available unless: You belong to Summer'21_Section L
Restricted Not available unless: You belong to Summer'21_Section K
Restricted Not available unless: You belong to Summer'21_Section L
Restricted Not available unless: You belong to Summer'21_Section M
- Course Details
Course Details
Course Rationale
An introduction to data mining; Data preparation, model building, and data mining techniques such as clustering, decisions trees and neural networks; Induction of predictive models from data: classification, regression, and probability estimation; Application
case studies; Data-mining software tools review and comparison.
Course Objective
- To appreciate the necessity of data mining in
everyday life
- To apply the concept of data mining in solving
problems
- To demonstrate applications of data mining
using tools
- To apply knowledge of data mining in project
work
Course Learning Outcomes (CLO's)
|
CLO1
|
Able to conceptualize basic applications, concepts, and techniques of data mining
|
|
CLO2
|
Able to identify appropriate data mining algorithms to solve real world problems
|
|
CLO3
|
Able to compare and evaluate different data mining techniques like classification, prediction, clustering and association rule mining
|
|
CLO4
|
Able to apply knowledge of data mining in developing research ideas
|
Evaluation Criteria
- Attendance: 7%
- Class Tests/Quizzes: 15%
- Assignment: 5%
- Presentation (using video/ppt): 8%
- Midterm Exam: 25%
- Final Exam: 40%
[All assessment will be based on onsite/online situation]
Recommended Books / Readings
Resources
Welcome Message
- Week 1: Introduction
Week 1: Introduction
Topics of Discussion
- Introduction to data mining
- Relationship to data warehousing
- Why data mining is a discipline?
- Overview of data mining tasks: Clustering, Classifications, Rules learning etc
Expected Learning Outcome
- Appreciation of the needs of data mining
- Visualization of data warehouse and relationship to data mining
- Visualization of different data mining tasks
Learning Materials
- Week 2: Working with Data
Week 2: Working with Data
Topics of Discussion
- Review of data mining task and related application examples
- Data Warehousing Introduction
- Course Project
Team and discussion
Expected Learning Outcome
- Identification
of data mining task
- Understand the Nature of Data
-
Able
to
Classify
the
Data
Based
on
their
Patterns
Learning Materials
- Week 3: Data Exploration
Week 3: Data Exploration
Topics of Discussion
- Discussion on data mining process: Data
preparation and cleansing and task identification
- Project Discussion
and execution plan
Expected Learning Outcome
- Visualization data mining processes
- Selection of
project topic by team
Learning Material
- Week 4: Classification and Prediction
Week 4: Classification and Prediction
Topics of Discussion
- Classification and Prediction
- Classification: tree-based approaches
Expected Learning Outcome
- Problem solving for classification and prediction
- Using Weka and
other DM tools
Learning Material
- Week 5: Classification Tuning
Week 5: Classification Tuning
Topics of Discussion
- Classification and Prediction with tuning
- Classification: tree-based approaches
- Course Project Presentation 1
Expected Learning Outcome
- Problem-solving for classification and prediction
- Using Weka and other DM tools
Learning Material
- Week 6: Nearest Neighbor and Bayesian Classification
Week 6: Nearest Neighbor and Bayesian Classification
Topics of Discussion
- Nearest Neighbor Classifier
- Bayesian Classification
Expected Learning Outcome
- Understand nearest neighbor classification
Learning Material
- Week 7: Mid Exam
Week 7: Mid Exam
Topics to be covered in midterm exam:
- Data Pre-processing
- Classification and Prediction
Instructions (Read Carefully):
- Students need to go through the
CASE STUDY shown in this exam paper.
- Analyze and answer specific
section based on your own thinking and work.
- Do not share as this will be
treated as plagiarism by Blended Learning Center.
- The Answer Script must be
Submitted in Provided Prescribed Format only.
- The File must be Named as ‘YourSection_YourId.pdf’.
- Answer Script can be Submitted
either using BLC or Google Form (No Multiple Submission).
- There will be No extension in
Time. So, Submit within 4:00 pm
CSE 450 K Script Submission (Google Form) URL
Restricted Not available unless: You belong to Summer'21_Section K
CSE 450 K Script Submission (BLC) Assignment
Restricted Not available unless: You belong to Summer'21_Section K
CSE 450 L Script Submission (Google Form) URL
Restricted Not available unless: You belong to Summer'21_Section L
CSE 450 L Script Submission (BLC) Assignment
Restricted Not available unless: You belong to Summer'21_Section L
CSE 450 M Script Submission (Google Form) URL
Restricted Not available unless: You belong to Summer'21_Section M
CSE 450 M Script Submission (BLC) Assignment
Restricted Not available unless: You belong to Summer'21_Section M
- Week 8: Association Rule Mining
Week 8: Association Rule Mining
Topics of Discussion
- Association Rule Mining
- Problem Solving using association rule mining
Expected Learning Outcome
- Apply the knowledge of association rule mining
Lecture Slides
- Week 9: Working with Clustering
Week 9: Working with Clustering
Topics of Discussion
- Clustering Data
- Problem Solving using Clustering
Expected Learning Outcome
- Understanding of clustering in data mining
- Problem-solving using clustering
Study Materials
- Week 10: Articial Neural Network
Week 10: Articial Neural Network
Topics of Discussion
- Artificial Neural Network
- Application of Neural Network
Expected Learning Outcome
- Apply knowledge of neural network
- Problem solving
Quiz 2
Restricted Not available unless: You belong to Summer'21_Section K
Quiz 2
Restricted Not available unless: You belong to Summer'21_Section L
Quiz 2
Restricted Not available unless: You belong to Summer'21_Section M
- Week 11: Course Project Discussion
Week 11: Course Project Discussion
Topics of Discussion
- Working with Data Mining Projects
Expected Learning Outcome
- Ability to apply data mining knowledge in development project
Course Assignment
Restricted Not available unless: You belong to Summer'21_Section K
- You have to summarize the understanding about the following topics in your own words:
- Data Exploration
- Data Visualization
- Classification
- Prediction
- Clustering
- Association Rule Mining
- Artificial Neural Network
- Any kind of plagiarism will be penalized.
- Don't Copy from Slides, write your own understanding.
- The document should be in either doc/docx/pdf format.
- The name of the file should contain your section and id.
Course Assignment
Restricted Not available unless: You belong to Summer'21_Section M
- You have to summarize the understanding about the following topics in your own words:
- Data Exploration
- Data Visualization
- Classification
- Prediction
- Clustering
- Association Rule Mining
- Artificial Neural Network
- Any kind of plagiarism will be penalized.
- Don't Copy from Slides, write your own understanding.
- The document should be in either doc/docx/pdf format.
- The name of the file should contain your section and id.
- Week 12: Review Week
- Week 13: Final Exam
Week 13: Final Exam
Topics to be included in final exam:
- Decision Tree Classifier
- Association rule mining
- Clustering and applications
- Artificial Neural Networks
CSE 450 K Script Submission Assignment
Restricted Not available unless: You belong to Summer'21_Section K
CSE 450 L Script Submission Assignment
Restricted Not available unless: You belong to Summer'21_Section L
CSE 450 M Script Submission Assignment
Restricted Not available unless: You belong to Summer'21_Section M