| Lecture
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Topic
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Lecture Slides
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Readings
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| Unit 1 | style="width: 60%" | Introduction: 1.1 - What Is Data Mining? 1.2 Challenges 1.3 Data Mining Origins 1.4 Data Mining Tasks
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1Intro.pdf
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Chapter 1 (CB1)
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| Unit 2
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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
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2DMT.pdf
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Chapter 2 (CB1)
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| Unit 3
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Association Rules: 6.1-Problem definition, 6.2-Frequent itemset generation, 6.3-Rule generation till Pg 351
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3AR.pdf
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Chapter 6 (CB1)
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| Unit 4
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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
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4Classification.pdf
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Chapter 4 (CB1)
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| Unit 5
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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
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Read from Authors' web page
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Chapter 5 (CB1)
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| Unit 6 / Chapter 8
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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
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5CA.pdf
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Chapter 8 (CB1)
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