Difference between revisions of "Fall 2026: Data Mining-1"

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== '''Lab 1: ''' ( week of 25<sup>th</sup> August & 01<sup>st</sup> September 2025  ) ==
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| style="width: 8%"  style="text-align: center;  | 1
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| style="width: 60%" | Apply data cleaning techniques on any dataset (e.g., Paper Reviews dataset in UCI repository). Techniques may include handling missing values, outliers and inconsistent values. A set of validation rules can be prepared based on the dataset and validations can be performed.
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| style="width: 15%" |  25/08/2025 - 01/09/2025
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| 02/09/2025
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== '''Lab 2: ''' ( week of 08<sup>th</sup> & 15<sup>th</sup> September 2025  ) ==
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| style="width: 8%" style="text-align: center;  | 2
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| style="width: 60%" | Apply data pre-processing techniques such as standardization/normalization, transformation, aggregation, discretization/binarization, sampling etc. on any dataset
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| style="width: 15%" |  08/09/2025 - 15/09/2025
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| 22/09/2025
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== '''Lab 3: ''' ( week of 22<sup>nd</sup> September 2025  ) ==
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| style="width: 8%" style="text-align: center;  | 5
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| style="width: 60%" | Apply simple K-means algorithm for clustering any dataset. Compare the performance of clusters by varying the algorithm parameters. For a given set of parameters, plot a line graph depicting MSE obtained after each iteration.
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| style="width: 15%" |  22/09/2025 - 06/10/2025
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| 06/10/2025
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== Resources ==
 
== Resources ==

Revision as of 17:28, 29 July 2026

Logistics

  • Class Timings: Mondays 10:30 am - 12:30 pm Thursdays 8:30 am - 9:30 am
  • Classroom: Mondays (T-7) and Thursdays(T-11)
  • Lab Timings: Mondays 1:30 pm - 3:30 pm
  • Labs: Computer Lab 4 (CL-4)

Course Overview

Lectures

Lecture Topic Lecture Slides Readings
Unit-1 Introduction to Data Mining: [unit1.pdf] Chapter 1
Unit 2 Data Pre-Processing: [unit2.pdf] Chapter 2
Unit 3 Cluster Analysis: [unit3.pdf] Chapter 5
Unit 4 Association Rule Mining: [unit4.pdf] Chapter 4
Unit 5 Classification: [unit5.pdf] Chapter 3 & 6

Assignments and Tests

Class Assignments

  • Assignment No. 1,
  • Assignment No. 2,

Tests and Quizzes

  • Test 1 :
  • Test 2 :

Labs

Instructions

  • Please be on time to avoid the Attendance Penalty.
  • Please put your mobile phone on Silent Mode.
  • Each lab assignment needs to be submitted in the Google Classroom for evaluation(will be notified in the GC lab-wise, submit before the deadline).
  • Turn off(shut down) your assigned computer and arrange the chair before you leave the lab.

Lab 0: Getting Started ( week of 03rd, 10th & 17th August 2026 )

Task No. Task Assessment Period. Submission Deadline
1 https://www.cse.msu.edu/~ptan/dmbook/tutorials/tutorial1/tutorial1.html -- --
2 https://www.cse.msu.edu/~ptan/dmbook/tutorials/tutorial2/tutorial2.html -- --
3 https://www.cse.msu.edu/~ptan/dmbook/tutorials/tutorial3/tutorial3.html -- --

Lab 1: ( week of 25th August & 01st September 2025 )

Task No. Task Assessment Period. Submission Deadline
1 Apply data cleaning techniques on any dataset (e.g., Paper Reviews dataset in UCI repository). Techniques may include handling missing values, outliers and inconsistent values. A set of validation rules can be prepared based on the dataset and validations can be performed. 25/08/2025 - 01/09/2025 02/09/2025

Lab 2: ( week of 08th & 15th September 2025 )

Task No. Task Assessment Period. Submission Deadline
2 Apply data pre-processing techniques such as standardization/normalization, transformation, aggregation, discretization/binarization, sampling etc. on any dataset 08/09/2025 - 15/09/2025 22/09/2025

Lab 3: ( week of 22nd September 2025 )

Task No. Task Assessment Period. Submission Deadline
5 Apply simple K-means algorithm for clustering any dataset. Compare the performance of clusters by varying the algorithm parameters. For a given set of parameters, plot a line graph depicting MSE obtained after each iteration. 22/09/2025 - 06/10/2025 06/10/2025


Resources

References:

  • Text Book: Tan P.N., Steinbach M, Karpatne A. and Kumar V. Introduction to Data Mining, Second edition, Sixth Impression, Pearson, 2023.

Additional References:

  1. Han J., Kamber M. and Pei J. Data Mining: Concepts and Techniques, 3rd edition, 2011, Morgan Kaufmann Publishers.
  2. Zaki M. J. and Meira J. Jr. Data Mining and Machine Learning: Fundamental Concepts and Algorithms, 2nd edition, Cambridge University Press, 2020.
  3. Aggarwal C. C. Data Mining: The Textbook, Springer, 2015
  4. Insight into Data mining: Theory and Practice, Soman K. P., Diwakar Shyam, Ajay V., PHI 2006
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