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Topic outline
- Welcome to Data Mining
Welcome to Data Mining
Course Code: CSE 450
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Course Title: Data Mining
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Program: BSC in CSE
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Faculty: Faculty of Science and
Information Technology (FSIT)
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Semester: FALL 2021
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Year: 2021
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Credit: 3
Course Hours: 3 hrs./week
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Course Level: Level 4, Term 2
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Course Category: Core
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Instructor : Dr. Fizar Ahmed Office : Room 411, CSE Building, Daffodil Tower Office Hour : Saturday to Wednesday (9:00AM to 5 PM) Telephone : 01775695814 Email : fizar.cse@diu.edu.bd Appointment in Google Calendar: Click Here
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Welcome Information on Data Mining Course
- Welcome Audio
- Listen to Course Objectives
- Listen to Expected Outcomes
- Listen to Course Delivery Plan
- Some Successful Projects
Student Interests Survey
Online Python Compiler for Data Mining Lab
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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 Objectives
- 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 Outcome: (at the end of the course, student will be able to do:)
CLO1
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Able to conceptualize basic applications, concepts, and techniques of
data mining
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CLO2
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Able to identify appropriate data mining algorithms to solve real
world problems
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CLO3
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Able to compare and evaluate different data mining techniques like
classification, prediction, clustering and association rule mining
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CLO4
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Able to apply knowledge of data mining in developing research ideas
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Grading Scheme
Attendance: 7% Class Tests/Quizes: 15%
Assignment: 5% Presentation (using video/ppt): 8%
Midterm Exam: 25% Final Exam: 40%
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- Introduction to Data Mining by Tan
- Reference Reading Materials
- Introduction to Data Mining and Applications
- Data Mining Concepts and Techniques
- Data Mining Techniques
- Data Mining using Weka
- Weka Manual
- Data Mining using Python
- Global Data Repository for Data Mining
- WISDM
- UCI ML Repository
- KDD Cup
- Kaggle
- KDnuggets
- IEEE Template
- ACM Template
- 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
- 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
- Team formation for the course project
- 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
- 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
- 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
- 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
- Problem solving using Weka
- Week 7: Mid Exam
Week 7: Mid Exam
Topics to be covered in midterm exam:
- Data Pre-processing
- Classification and Prediction
Please follow the name convention of the answer script file name: Section_StudentID_Name_SubjectCode_FALL2021_mid.pdf
- 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
- Problem solving using Weka
- Week 9: Working with Clustering
Week 9: Working with Clustering
Topics of Discussion
- Clustering Data
- Problem Solving using Clustering
- Course Project Presentation 2
Expected Learning Outcome
- Understanding of clustering in data mining
- Problem solving using clustering
- Week 10: Neural Network
Week 10: Neural Network
Topics of Discussion
- Neural Network
- Application of neural network
Expected Learning Outcome
- Apply knowledge of neural network
- Problem solving
- Week 11: Course Project Discussion
Week 11: Course Project Discussion
Topics of Discussion
Expected Learning Outcome
- Ability to apply data mining knowledge in development project
- Final Examination Summer 2021
Final Examination Summer 2021