List of Reference Books for Data Mining- B.Tech 3rd Year
- Introduction to Data Mining: Pang-Ning Tan & Michael Steinbach, Vipin Kumar, Pearson.
- Data Mining concepts and Techniques, 3/e, Jiawei Han, Michel Kamber, Elsevier.
- The Data Mining Techniques and Applications: An Introduction, Hongbo Du, Cengage Learning.
- Data Mining : Vikram Pudi and P. Radha Krishna, Oxford.
- Data Mining and Analysis – Fundamental Concepts and Algorithms; Mohammed J. Zaki, Wagner Meira, Jr, Oxford
- Data Warehousing Data Mining & OLAP, Alex Berson, Stephen Smith, TMH.
Data mining Syllabus for B.Tech 3rd Year
OBJECTIVES:
• Students will be enabled to understand and implement classical models and algorithms in data warehousing and data mining.
• They will learn how to analyze the data, identify the problems, and choose the relevant models and algorithms to apply.
• They will further be able to assess the strengths and weaknesses of
various methods and algorithms and to analyze their behavior.
UNIT –I:
Introduction:
Why Data Mining? What Is Data Mining?1.3 What Kinds of Data Can Be
Mined?1.4 What Kinds of Patterns Can Be Mined? Which Technologies Are
Used? Which Kinds of Applications Are Targeted? Major Issues in Data
Mining. Data Objects and Attribute Types, Basic Statistical Descriptions
of Data, Data Visualization, Measuring Data Similarity and
Dissimilarity
UNIT –II:
Data
Pre-processing: Data Preprocessing: An Overview, Data Cleaning, Data
Integration, Data Reduction, Data Transformation, and Data
Discretization
UNIT –III:
Classification:
Basic Concepts, General Approach to solving a classification problem,
Decision Tree Induction: Working of Decision Tree, building a decision
tree, methods for expressing attribute test conditions, measures for
selecting the best split, Algorithm for decision tree induction.
UNIT –IV:
Classification: Alternative Techniques, Bayes’ Theorem, Naïve Bayesian Classification, Bayesian Belief Networks
UNIT –V:
Association
Analysis: Basic Concepts and Algorithms: Problem Defecation, Frequent
Item Set generation, Rule generation, compact representation of frequent
item sets, FP-Growth Algorithm. (Tan & Vipin)
UNIT –VI:
Cluster
Analysis: Basic Concepts and Algorithms: Overview: 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, Strengths, and Weaknesses;
Agglomerative
Hierarchical Clustering: Basic Agglomerative Hierarchical Clustering
Algorithm DBSCAN: Traditional Density Center-Based Approach, DBSCAN
Algorithm, Strengths, and Weaknesses. (Tan & Vipin)
OUTCOMES:
• Understand stages in building a Data Warehouse
• Understand the need and importance of preprocessing techniques
• Understand the need and importance of Similarity and dissimilarity techniques
• Analyze and evaluate the performance of algorithms for Association Rules.
• Analyze Classification and Clustering algorithms
