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

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DM1, 2026
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== 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 ==
 +
* As per the Delhi University Course [https://drive.google.com/file/d/1esk508_yQg70fvwHWgORjNqhBqDSR8MN/view Syllabus/Guidelines]
 +
 
 +
== Lectures ==
 +
{| class="wikitable" style="text-align: left; width: 100%";
 +
|-
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!Lecture
 +
!Topic
 +
!Lecture Slides
 +
!Readings
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|-
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| style="width: 12%; " |  Unit-1
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| style="width: 60%" |  '''''Introduction to Data Mining:'''''
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| style="width: 15%" | [unit1.pdf] 
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| Chapter 1
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|-
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| Unit 2
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|  '''''Data Pre-Processing:'''''
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|  [unit2.pdf] 
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| Chapter 2
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|-
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| Unit 3
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|  '''''Cluster Analysis:'''''
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|  [unit3.pdf] 
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| Chapter 5
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|-
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| Unit 4
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|  '''''Association Rule Mining:'''''
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|  [unit4.pdf]   
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| Chapter 4
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|-
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| Unit 5
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|  '''''Classification:'''''
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| [unit5.pdf] 
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| Chapter 3 & 6
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|}
 +
 
 +
== Assignments and Tests==
 +
===Class Assignments===
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* '''''Assignment No. 1''''',  
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* '''''Assignment No. 2''''',
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===Tests and Quizzes===
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* '''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 03<sup>rd</sup>, 10<sup>th</sup > & 17<sup>th</sup >  August 2026 ) ===
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{| class="wikitable" style="text-align: justify; 
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|-
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! Task No. 
 +
! Task 
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! Assessment Period. 
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! Submission Deadline
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|-
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| style="width: 8%"  | 1
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| style="width: 60%" | https://www.cse.msu.edu/~ptan/dmbook/tutorials/tutorial1/tutorial1.html
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| style="width: 15%" |  --
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| --
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|-
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| 2 || https://www.cse.msu.edu/~ptan/dmbook/tutorials/tutorial2/tutorial2.html  || -- || --
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|-
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| 3 || https://www.cse.msu.edu/~ptan/dmbook/tutorials/tutorial3/tutorial3.html || -- || --
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|}
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=== '''Lab 1: ''' ( week of 24<sup>th</sup> & 31<sup>st</sup> August 2026  ) ===
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{| class="wikitable" style="text-align: justify; 
 +
|-
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! Task No. 
 +
! Task 
 +
! Assessment Period. 
 +
! Submission Deadline
 +
|-
 +
| style="width: 8%"  style="text-align: center;  | 1
 +
| 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%" |  24/08/2026 - 31/08/2026
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| 01/09/2026
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|}
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=== '''Lab 2: ''' ( week of 07<sup>th</sup> & 14<sup>th</sup> September 2026  ) ===
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{| class="wikitable" style="text-align: justify; 
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|-
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! Task No. 
 +
! Task 
 +
! Assessment Period. 
 +
! Submission Deadline
 +
|-
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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%" |  07/09/2026 - 14/09/2026
 +
| 22/09/2025
 +
|}
 +
 
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=== '''Lab 3: ''' ( week of 21<sup>st</sup> & 28<sup>th</sup>  September 2026  ) ===
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{| class="wikitable" style="text-align: justify; 
 +
|-
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! Task No. 
 +
! Task 
 +
! Assessment Period. 
 +
! Submission Deadline
 +
|-
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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%" |  21/09/2026 - 28/09/2026
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| 29/09/2026
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|}
 +
 
 +
== 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:'''
 +
#  Han J., Kamber M. and Pei J. Data Mining: Concepts and Techniques, 3rd edition, 2011, Morgan Kaufmann Publishers.
 +
#  Zaki M. J. and Meira J. Jr. Data Mining and Machine Learning: Fundamental Concepts and Algorithms, 2nd edition, Cambridge University Press, 2020.
 +
#  Aggarwal C. C. Data Mining: The Textbook, Springer, 2015
 +
#  Insight into Data mining: Theory and Practice, Soman K. P., Diwakar Shyam, Ajay V., PHI 2006

Latest revision as of 17:36, 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 24th & 31st August 2026 )

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. 24/08/2026 - 31/08/2026 01/09/2026

Lab 2: ( week of 07th & 14th September 2026 )

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 07/09/2026 - 14/09/2026 22/09/2025

Lab 3: ( week of 21st & 28th September 2026 )

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. 21/09/2026 - 28/09/2026 29/09/2026

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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