Syllabus DMDW, DWHM Question papers, Answers, important Question DATA WARE HOUSING AND MINING R13 Regulation B.Tech JNTUK-kakinada Syllabus download

Syllabus ,DMDW, DWHM Question papers, Answers, important Question DATA WARE HOUSING AND MINING, R13 Regulation, B.Tech , JNTUK,Syllabus, download,

Data Warehousing and Mining Syllabus R13 Regulation unit wise

Unit-I

Introduction :

• What Motivated Data Mining?Why Is It Important?
• Data Mining—On What Kind of Data
• Data Mining Functionalities—What Kinds of Patterns Can Be Mined?
• Are All of the Patterns Interesting?
• Classification of Data Mining Systems
• Data Mining Task Primitives
• Integration of a Data Mining System with a Database or Data Warehouse System
• Major Issues in Data Mining

Unit-II

Data Pre-processing :

• Why Pre-process the Data?
• Descriptive Data Summarization
• Data Cleaning
• Data Integration and Transformation
• Data Reduction
• Data Discretization and Concept Hierarchy Generation

Unit-III

Data Warehouse and OLAP Technology:

An Overview :

• What Is a Data Warehouse?
• A Multidimensional Data Model
• Data Warehouse Architecture
• Data Warehouse Implementation
• From Data Warehousing to Data Mining

Unit-IV

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

Model Over fitting:

• Due to presence of noise
• due to lack of representation samples

Evaluating the performance of classifier:

• holdout method
• random sub sampling
• cross-validation
• bootstrap

Unit-V

Association Analysis:

Basic Concepts and Algorithms :

• Introduction
• Frequent Item Set generation
• Rule generation
• compact representation of frequent item sets
• FP-Growth Algorithm

Unit-VI

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 Kmeans
• 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. Data Mining Techniques and Applications: An Introduction, Hongbo Du, Cengage Learning.
2. Data Mining : Introductory and Advanced topics : Dunham, Pearson.
3. Data Warehousing Data Mining & OLAP, Alex Berson, Stephen Smith, TMH.
4. Data Mining Techniques, Arun K Pujari, Universities Press.
5. Data Mining concepts and Techniques, 3/e, Jiawei Han, Michel Kamber, Elsevier
6. Introduction to Data Mining : Pang-Ning Tan & Michael Steinbach, Vipin Kumar, Pearson

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