Topic outline

  • Welcome to Data Mining



    Welcome Message


    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 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 possess the basic knowledge of Weka and Python concerning data mining and machine learning
    • CO2 Able to implement different data mining and machine learning algorithms like classification, prediction, clustering and association rule mining to solve real-world problems using Weka and Python
    • CO3 Able to compare and evaluate different data mining and machine learning algorithms like classification, prediction, clustering and association rule mining using Weka and/or Python
    • CO4 Able to apply implementation knowledge of data mining and machine learning in developing research ideas
    Grading Scheme
    Attendance: 10%
    Lab Performance: 25%
    Project / Lab Report: 25%
    Final Exam: 40%

    • Recommended Books
    1. Introduction to Data Mining and Applications
    2. Data Mining Concepts and Techniques
    3. Data Mining Techniques
    4. Data Mining Using Weka
    5. Weka Manual
    6. Data Mining Using Python
    • Global Data Repository for Data Mining and/or Machine Learning
    1. WISDM
    2. UCI ML Repository
    3. KDD Cup
    4. Kaggle
    5. KDnuggets

    • Standard Templates
    1. IEEE Template
    2. ACM Template

  • Week 1: Introduction to Weka

    Topics of Discussion

    • Introduction to Weka
    • Relationship to data mining
    • Overview of data mining with Weka


    Expected Learning Outcome

    • Appreciation of the needs of data mining with Weka
    • Visualization of the relationship of Weka to data mining
    • Visualization of different data mining tasks with Weka

  • Week 2: Data Visualization Using Weka

    Topics of Discussion

    • Review of data mining task and related application examples
    • Data Visualization with Weka
    • Course Project Team and discussion


    Expected Learning Outcome

    • On-hand acquaintance and practice of data visualization with Weka
    • Team formation for the course project

  • Week 3: Feature/Attribute Selection Using Weka

    Topics of Discussion

    • Discussion on feature/attribute selection 
    • Project Discussion and execution plan

    Expected Learning Outcome

    • On-hand acquaintance and practice of feature/attribute selection with Weka
    • Selection of project topic by team

  • Week 4: Classification Using Weka

    Topics of Discussion

    • Classification and prediction with Weka
    • Classification: decision tree

    Expected Learning Outcome

    • Problem solving skill in classification and prediction
    • Skill in using Weka as a data mining tool for classification and prediction

  • Week 5: Continuation of Classification Using Weka

    Topics of Discussion

    • Classification and prediction with Weka
    • Classification: Bayesian, Instance-based

    Expected Learning Outcome

    • Problem solving skill in classification and prediction
    • Skill in using Weka as a data mining tool for classification and prediction

  • Week 6: Cluster Analysis Using Weka

    Topics of Discussion

    • Cluster Analysis with Weka
    • Cluster Analysis: partitional (K-means), hierarchical, density-based


    Expected Learning Outcome

    • Problem solving skill in classification and prediction
    • Skill in using Weka as a data mining tool for cluster analysis

  • Week 7: Presentation of Project # 1 (Using Weka)

    Project # 1 (with Weka) Presentation



    • Presentation Upload Assignment

      Upload your recorded video on google drive and set the privacy to anyone with the link. And share that google drive link here. It is not a group task.

  • Week 7: Mid Exam

    Midterm Examination Week




    • Week 9: Introduction to Python

      Topics of Discussion

      • Introduction to Python
      • Relationship to machine learning
      • Overview of machine learning with Python

      Expected Learning Outcome

      • Appreciation of the needs of machine learning with Python
      • Visualization of the relationship of Python to machine learning
      • Visualization of different machine learning tasks with Python

    • Week 10: Classification Using Python


      Topics of Discussion

      • Classification and prediction with Python
      • Classification: decision tree
      • Classification: K nearest Neighbore

      Expected Learning Outcome

      • Problem solving skill in classification and prediction
      • Skill in using Weka as a data mining tool for classification and prediction

    • Week 12: Cluster Analysis Using Python


      Topics of Discussion

      • Cluster Analysis with Python
      • Cluster Analysis: partitional (K-means), hierarchical, density-based


      Expected Learning Outcome

      • Problem solving skill in classification and prediction
      • Skill in using Weka as a data mining tool for cluster analysis
      • Ability to apply data mining knowledge in development project

    • Report Submission

      You have to include the following things in your report. 

      • Describe your dataset(Mainly visualization)
      • Describe the algorithm you have used for your project(both classification and clustering)
      • Discuss result(with proper graphs and images) and accuracy
      • Discuss a solution how accuracy can be increased

      On the top page, you have to write the following points:

      1. Course title and name
      2. Group members' info(name, id, section name)
      3. Course teachers' detail
      4. Date of submission

      Upload your report as a pdf file and rename it as "ID of group member_section name". From each group, one person will upload the report not everyone. The deadline is 18 August at 23:59 pm. Deadline time will be strictly followed.


    • Topic 14