Course Name: Data Mining
Course Number: BO CDA 309
Course Credit: 3-0-0-3
Introduction: Data Mining, Data Mining Functionalities— Data Mining Task Primitives, , Major Issues in Data Mining.
Data Pre-processing: Descriptive Data Summarization, Data Cleaning, Data Integration and Transformation, Data Reduction, Data Discretization and Concept Hierarchy Generation
Association Analysis: Basic Concepts and Algorithms: Introduction, Frequent Item Set generation, Rule generation, compact representation of frequent item sets, FP-Growth Algorithm
Classification: Basic Concepts, General Approach to solving a classification problem, Decision Tree Induction: Working of Decision Tree, building a decision tree, methods for expressing an attribute test conditions, measures for selecting the best split, Algorithm for decision tree induction.
Classification: Alterative Techniques, Bayes’ Theorem, Naïve Bayesian Classification, Bayesian Belief Networks
Cluster Analysis: Basic Concepts and Algorithms: What Is Cluster Analysis? Different Types of Clustering, Different Types of Clusters, K-means, The Basic K-means Algorithm, K-means: Additional Issues, Bisecting K-means, K-means and Different Types of Clusters, Strengths and Weaknesses, K-means as an Optimization Problem, Agglomerative Hierarchical clustering, Basic Agglomerative Hierarchical clustering Algorithm, Specific Techniques, DBSCAN, Traditional Density: center-Based Approach, The DBSCAN Algorithm, Strengths and Weaknesses
Reference Books :
1. Pang- Ning Tan, Michael Steinbach and Vipin Kumar, Introduction to Data Mining, Pearson, 2016.
2. Jiawei Han, Michel Kamber, Data Mining concepts and Techniques, 3rd Edition, Morgan Kaufmann, 2011.
3. Hongbo Du, Data Mining Techniques and Applications: An Introduction, Cengage India Private Limited, 2013.
4. Margaret Dunham, Data Mining :Introductory and Advanced topics, Pearson, 2002.
5. Alex Berson, Stephen Smith, Data Warehousing Data Mining & OLAP, TMH, 2017.