Spring 2023: Data Mining

Logistics

• Class Timings: Wednesdays 1:00 pm - 3:00 pm (5^th and 6^th slot)and Thursdays 10:45 am - 12:45 pm (3^rd and 4^th slot)
• Classroom: R33
• Lab Timings: Mondays 8:45 am - 12:45 pm (1^st - 4^thslots)
• Labs: CS Lab 5

Course Overview

• As per the Delhi University Course Guidelines

Lectures

Lecture	Topic	Lecture Slides	Readings
Unit 1 / Chapter 1	Introduction: 1.1 - What Is Data Mining? 1.2 Challenges 1.3 Data Mining Origins 1.4 Data Mining Tasks	1Intro.pdf	Chapter 1 (CB1)
Unit 2 / Chapter 2	Data mining techniques: 2.1- Types of data, 2.2 – Data Quality, 2.3.1 Aggregation, 2.3.2 Sampling, 2.3.3 Dimensionality reduction – upto pg 51, 2.3.4 Feature subset selection upto pg 52, 2.4.5 Feature creation upto pg 55, 2.3.6 Discretization upto pg 59, 2.3.7 variable transformations 2.4.3 Dissimilarity among data objects 2.4.4 similarity among data objects	2DMT.pdf	Chapter 2 (CB1)
Unit 5 / Chapter 6	Association Rules: 6.1-Problem definition, 6.2-Frequent itemset generation, 6.3-Rule generation till Pg 351	3AR.pdf	Chapter 6 (CB1)
Unit 3 / Chapter 4	Classification: Basic Concepts and Techniques: 4.1 – Preliminaries, 4.2 – General Approach to Solving a Classification Problem, 4.3 Decision Tree Induction (Till Pg. 165), 4.5 –Evaluating the Performance of a Classifier	4Classification.pdf	Chapter 4 (CB1)
Unit 4 / Chapter 5	Classification: Alternative Techniques: 5.1 – Rule Based Classifier (upto page 212),5.2 – Nearest Neighbor Classifiers, 5.3–Bayesian Classifiers (Complete for discrete data and only introduction of Bayes classifier for continuous attributes) till pg. 233, 5.7.1 –Alternative Metrics	Read from Authors' web page	Chapter 5 (CB1)
Unit 6 / Chapter 8	Clustering: 8.1 Basic concepts of clustering analysis, 8.2 K-Means (8.2.1-8.2.5 except 8.2.3), 8.3 Agglomerative Hierarchical Clustering (except pg 522-524), 8.4 DBSCAN	5CA.pdf	Chapter 8 (CB1)

Assignments and Tests

Class Assignments

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

Tests and Quizzes

• Test 1 :
• Test 2 :

Resources

Course Books:

• CB1: Introduction to Data Mining, Pang-Ning Tan, Michael Steinbach, Vipin Kumar, Pearson Education.
  

References:

• R2: Data Mining: Concepts and Techniques, 3nd edition,Jiawei Han and Micheline Kamber.
  
• R3: Data Mining: A Tutorial Based Primer, Richard Roiger, Michael Geatz, Pearson Education 2003.
  
• R4: Introduction to Data Mining with Case Studies, G.K. Gupta, PHI 2006.
  
• R5: Insight into Data mining: Theory and Practice, Soman K. P., DiwakarShyam, Ajay V., PHI 2006