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

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== Resources ==
 
== Resources ==
 
'''References:'''
 
'''References:'''
−
* '''R1''': Dejey, S. Murugan, Cyber Forensics, Oxford University Press, 2018.
+
* '''Text Book''': Tan P.N., Steinbach M, Karpatne A. and Kumar V. Introduction to Data Mining, Second edition, Sixth Impression, Pearson, 2023.
−
* '''R2''':  C. Altheide & H. Carvey, Digital Forensics with Open Source Tools, Syngress, 2011. ISBN: 9781597495868.
 
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* '''R3''':  Niranjan Reddy, Practical Cyber Forensics. An Incident-Based Approach to Forensic Investigations, Apress, 2019 (Available on DU eLibrary).
 
−
* '''R4''':  Marjee T. Britz, Computer Forensics and Cyber Crime: An Introduction, Pearson Education, 2013.
 
−
* '''R5''':  https://www.indiacode.nic.in/handle/123456789/1999?sam_handle=123456789/1362
 
  
 
'''Additional References:'''
 
'''Additional References:'''
−
#  “Computer Forensics: Investigating Network Intrusions and Cybercrime” by Cameron H.
+
#  Han J., Kamber M. and Pei J. Data Mining: Concepts and Techniques, 3rd edition, 2011, Morgan Kaufmann Publishers.
−
#  Malin, Eoghan Casey, and James M. Aquilina Online Course Management System: https://esu.desire2learn.com/
+
#  Zaki M. J. and Meira J. Jr. Data Mining and Machine Learning: Fundamental Concepts and Algorithms, 2nd edition, Cambridge University Press, 2020.
−
#  Computer Forensics, Computer Crime Investigation by John R., Vacca, Firewall Media, New Delhi.
+
#  Aggarwal C. C. Data Mining: The Textbook, Springer, 2015
−
# Computer Forensics and Investigations by Nelson, Phillips, Enfinger, Steuart, CENGAGE Learning
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# Insight into Data mining: Theory and Practice, Soman K. P., Diwakar Shyam, Ajay V., PHI 2006

Revision as of 17:26, 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 -- --

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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