Data Mining

Data Mining. Instructor: Prof. Pabitra Mitra, Department of Computer Science and Engineering, IIT Kharagpur. Data mining is study of algorithms for finding patterns in large data sets. It is an integral part of modern industry, where data from its operations and customers are mined for gaining business insight. It is also important in modern scientific endeavors. Data mining is an interdisciplinary topic involving, databases, machine learning and algorithms. The course will cover the fundamentals of data mining. It will explain the basic algorithms like data preprocessing, association rules, classification, clustering, sequence mining and visualization. It will also explain implementations in open source software. Finally, case studies on industrial problems will be demonstrated. (from


Lecture 01 - Introduction, Knowledge Discovery Process
Lecture 02 - Data Processing: Data Types and Attributes
Lecture 03 - Data Processing: Data Quality, Processing Steps
Lecture 04 - Association Rules
Lecture 05 - Association Rule: Apriori Algorithm
Lecture 06 - Apriori Algorithm: Rule Generation
Lecture 07 - Classification, Supervised Learning
Lecture 08 - Decision Tree Inference
Lecture 09 - Decision Tree Construction
Lecture 10 - Decision Tree Construction
Lecture 11 - Decision Tree Pruning and Extensions
Lecture 12 - Bayes Classifier: Class Conditional Probabilities
Lecture 13 - Bayes Classifier: Posterior Probability, MAP
Lecture 14 - Bayes Classifier: Multivariate Bayes
Lecture 15 - Bayes Classifier: Naive Bayes
Lecture 16 - Bayes Classifier: Conditional Independence
Lecture 17 - K-Nearest Neighbor Classifiers
Lecture 18 - K-Nearest Neighbor: Distance Function, Choice of K
Lecture 19 - K-Nearest Neighbor Classification Techniques
Lecture 20 - K-Nearest Neighbor: High Dimensional Search
Lecture 21 - K-Nearest Neighbor: Classifier Evaluation
Lecture 22 - Support Vector Machine: Linear Discriminant
Lecture 23 - Support Vector Machine: Separating Hyperplane
Lecture 24 - Support Vector Machine: Maximum Margin Hyperplane
Lecture 25 - Support Vector Machine: Dual Optimization Problem
Lecture 26 - Support Vector Machine: Support Vectors
Lecture 27 - Kernel Machines: Soft Margin Hyperplane, Kernels
Lecture 28 - Artificial Neural Networks: Perceptron
Lecture 29 - Artificial Neural Networks: Learning Rule
Lecture 30 - Artificial Neural Networks: Multilayer Perceptron
Lecture 31 - Artificial Neural Networks: Backpropagation
Lecture 32 - Basics of Clustering
Lecture 33 - Hierarchical Clustering
Lecture 34 - Clustering: K-Means
Lecture 35 - Clustering: DBSCAN
Lecture 36 - Clustering: Evaluation
Lecture 37 - Regression Problem
Lecture 38 - Linear Regression
Lecture 39 - Nonlinear Regression
Lecture 40 - Regression: Overfitting
Lecture 41 - Dimensionality Reduction: Feature Selection
Lecture 42 - Dimensionality Reduction: Principal Component Analysis
Lecture 43 - Tutorial

Data Mining
Instructor: Prof. Pabitra Mitra, Department of Computer Science and Engineering, IIT Kharagpur. The course will cover the fundamentals of data mining.