Data Warehouse & Mining

Learning Outcomes: 
Upon Completion of the course, the students will be able to
• Pre-process the data for mining applications
• Understand supervised and unsupervised mining
• Apply various frequent pattern mining techniques on market basket data
• Understand the importance of Attribute Selection (Curse of Dimensionality)
• Differentiate problems related to classification or clustering
• Design and deploy appropriate classification or clustering techniques
• Measure the quality of extracted patterns and knowledge using various evaluation methods
Syllabus: 
Unit NoTopics
1

Introduction

Introduction to Data Mining, Importance of Data Mining, Data Mining functionalities, Classification of Data mining systems, Data mining architecture, Major Issues in Data Mining, Data mining metrics, Applications of Data Mining, Social impacts of data, Data Mining from a Database Perspective

2

Data Pre-processing

Introduction, Descriptive Data Summarization, Data Cleaning, Data Integration and Transformation, Data Reduction, Data Discretization

3

Classification and Prediction

Basic issues regarding classification and predication, Classification by Decision Tree, Bayesian classification, classification by back propagation, Associative classification, Prediction, Statistical-Based Algorithms, Decision Tree -Based Algorithms, Neural Network -Based Algorithms, Rule-Based Algorithms, Other Classification Methods, Combining Techniques, Classifier Accuracy and Error Measures

4

Clustering

Similarity  and Distance Measures, Hierarchical Algorithms, Partitioned Algorithms, Clustering Large Databases, Clustering with Categorical Attributes

5

Association Rules

Basic Algorithms, Advanced Association Rule Techniques, Measuring the Quality of Rules

Text Books: 
Name : 
Data Mining: Concepts and Techniques
Author: 
J. Han and M. Kamber
Publication: 
Morgan Kaufman
Edition: 
3rd,2011
Reference Books: 
Name: 
Data Warehousing, Data Mining
Author: 
Alex Berson
Stephen J. Smith
Publication: 
MGH,1998
Syllabus PDF: 
branch: 
BDA
Course: 
2018
Stream: 
B.Tech