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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 nptel.ac.in)

Lecture 01 - Introduction, Knowledge Discovery Process

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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