Section outline
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Instructor: Md. Aynul Hasan Nahid
Office : Room # 712, Level 7, Daffodil Tower
Cellphone #: 01674834062
Email: aynul.cse@diu.edu.bd
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 Outcomes (CO’s)
- CO1 Able to grasp the basic Data Mining Principles
- CO2 Able to identify appropriate data mining algorithms to solve real world problems
- CO3 Able to compare and evaluate different data mining techniques like classification, prediction, clustering and association rule mining
- CO4 Able to apply data mining knowledge in problem solving
Grading Scheme Attendance: 7%
Class Tests/Quizzes: 15%
Assignment: 5%
Presentation (using video/ppt): 8%
Midterm Exam: 25%
Final Exam: 40%
- Text Book
- Introduction to Data Mining by Tan
- Machine Learning by Tom Mitchell
- Reference Reading Materials
- Introduction to Data Mining and Applications
- Data Mining Concepts and Techniques
- Data Mining Techniques
- Standard Templates
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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
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Topics of Discussion
- Review of data mining task and related application examples
- Data Warehousing Introduction
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Topics of Discussion
- Discussion on data mining process: Data preparation and cleansing and task identification
- Project Discussion and execution plan
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Topics of Discussion
- Classification and Prediction
- Classification: tree-based approaches
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Topics of Discussion
- Nearest-neighbor classifiers
- Bayesian classifiers
- Nearest-neighbor classifiers
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Opened: Sunday, 10 July 2022, 1:30 PMDue: Sunday, 10 July 2022, 4:00 PM
Submit your answer script here at the BLC course.
- In case of any unsolvable difficulty, submit the script to the Google classroom.
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Topics of Discussion
- Clustering Data
- Problem Solving using Clustering
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Topics of Discussion
- Association Rule Mining
- Problem Solving using association rule mining
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Topics of Discussion
- Association Rule Mining
- Problem Solving using association rule mining
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Topics of Discussion
- Neural Network
- Application of neural network
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- Research article
writing, review and publishing
- Review discussion and advanced topics.
- Research article
writing, review and publishing
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Topics to be included in final exam:
- Association rule mining
- Clustering and applications
- Nearest-Neighbor Classifier and Bayesian Classifier
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Opened: Wednesday, 31 August 2022, 1:30 PMDue: Wednesday, 31 August 2022, 5:00 PM