DATA MINING LAB

Paper Code: 
24MCA326
Credits: 
02
Periods/week: 
04
Max. Marks: 
100.00
Objective: 

The course enables the students to
•    Understand the implementation procedures for Data Mining algorithms.
•    Apply algorithms on appropriate data sets.
•    Compare performance of Mining algorithms based on same techniques
•    Identify and apply Data Mining algorithms to solve real world problems

Course Outcomes: 

Course

Learning Outcome (at course level)

 

Learning and teaching strategies

Assessment Strategies

Course Code

Course

Title

24MCA 326

Data Mining Lab

(Practical)

  1. Categorize and apply Data Mining algorithms to solve real world problems.
  2. Develop practical experience using data mining techniques on real world data sets.
  3. Apply data preprocessing tasks and Demonstrate performing association rule mining on data sets
  4. Demonstrate performing classification and Clustering  on data sets
  5. Compare performance of different algorithms
  6. Contribute effectively in course-specific interaction

Approach in teaching:

Interactive Lectures, Modeling, Discussions, implementing enquiry based learning, Student cantered approach, Through audio-visual aids

 

Learning activities for the students:

Experiential Learning, Presentations, Case based learning, Discussions, Quizzes and  Assignments

  • Assignments
  • Written tests in classroom
  • Classroom Activity
  • Objective Quiz
  • Semester End Exam

 

Unit I: 

1.    Perform data preprocessing tasks and Demonstrate performing association rule mining on data sets
2.    Generate Association Rules using the Apriori algorithm
3.    Generate Association Rules using the FP-Growth algorithm                                   4.    Study the association rules generated. Derive interesting insights and observe rule generation process.
5.    Demonstrate classification on data sets using Decision Tree algorithm
6.  Extract if-then rules from the decision tree generated by the classifier, Observe the confusion matrix
7. Perform Naïve-bayes classification. Interpret the results obtained and Compare classificationresults                                                                                                                 8. Implement k-Nearest Neighbor algorithm to classify data set and display both correct and incorrect predictions.                                                                                         9. Implement the Regression algorithm in order to fit data points. Apply it on an appropriate data set and draw graph.                                                                                                                10. Perform k-means clustering algorithm with different values of k (number of desired clusters). Study the clusters formed. Observe the sum of squared errors and centroid, and derive insights.                                                                                               11. Compare performance of different algorithms on real world data sets.

                                                                                                                      

Unit II: 
  1. Trevor Hastie Robert Tibshirani ,Jerome Friedman “The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition 2023
  2. Ethem Alpaydin, “Introduction to Machine Learning”, Prentice Hall of India, 2015
  3. Valliappa Lakshmanan Sara Robinson Michael Munn “Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps 1st Edition”2023
  4. Joel Grus, “Data Science from Scratch- First Principles with Python”, O’Reilly, 2015
  5. Christopher Bishop, “Pattern Recognition and Machine Learning”, Springer 2006
  6. Ethem Alpaydin, “Introduction to Machine Learning”, Prentice Hall of India, 2005
  7. Joel Grus, “Data Science from Scratch- First Principles with Python”, O’Reilly, 2015

 

Suggested Readings:

  1. Tom Mitchell, “ Machine Learning”, McGraw-Hill, 1997
  2. Stephen MarsLand, “Machine Learning-An Algorithmic Perspective”, CRC Press, 2009
  3. Kevin P. Murphy, “Machine Learning: A Probabilistic Perspective”, MIT Press, 2012
  4. M. Gopal, “Applied MACHINE LEARNING”, McGraw-Hill, 2018
  5. Mark Summerfield, “Programming in Python 3: A Complete Introduction to the Python Language”, Addison Wesley, 2010                                                                                                                                                                                                                                                                             E-resources:
  6. Data Mining and Analysis( https://online.stanford.edu/)
  7. Introduction to Machine Learning, By NPTEL, https://nptel.ac.in/courses/106106139
  8. Data Mining, Swayam , By Prof. Pabitra Mitra   |   IIT Kharagpur
  9. Data Mining Methods( https://www.coursera.org/ )                                                                                                                                                                                                                                                                               Journals (International / National):
  10.  International Journal of Data Warehousing and Mining (IJDWM)
  11. International Journal of Mining Science and Technology
  12. Machine Learning with Applications, By Elsevier, https://www.journals.elsevier.com/machine-learning-with-applications
  13. Journal of Machine Learning Research, By Microtome publishing http://www.mtome.com/Publications/JMLR/jmlr.html

 

Unit IV: 
Unit V: 
Academic Year: 
2024-25
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