Data Mining and Data Warehousing Lecture Notes Free Download

Data Mining and Data Warehousing Lecture Notes: Graduates hunting to get a hold of the Data Mining And Data Warehousing Lecture Notes and Study Materials can access the best resources inclusive of the important topics and concepts for their preparation process.

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Data Mining And Data Warehousing Reference Books
Data Mining And Data Warehousing Syllabus
List of Data Mining And Data Warehousing Important Questions
FAQs on Data Mining And Data Warehousing Lecture Notes Free Download
Conclusion

Introduction to Data Mining and Data Warehousing Notes

Data Warehouse not a product but is an environment. Data Warehousing is a process constructed by data integration from Multiple Heterogeneous sources that support structured queries, analytical reporting, and decision making. The only motivation to build a data warehouse is corporate data gathered from the scatterings found across different databases and available in different formats.

Data Mining is a process used by the majority of the companies to extract unknown, actionable, and valid raw data from a large set and turn it into useful information to make crucial business decisions. Data mining uses softwares to make patterns from large batches of data and develop them to a more effective and strategic place to increase sales and decrease costs.

Data Mining And Data Warehousing Lecture Notes Free Download

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Data Mining and Data Warehousing Reference Books

Books are well-researched and rich sources of information and data. Candidates should consult books that provide good conceptual background and can avail the best books for Data Mining And Data Warehousing as per the subject experts’ recommendations from this article.

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The list of best and highly recommended books for Data Mining And Data Warehousing preparation are as follows, and graduates can select the reference book that meets their knowledge and prepare accordingly.

Book on The Data Mining Techniques by Arun K Pujari
The Student Edition of Data Warehousing Fundamentals by Paulraj Ponnaiah
Book on Data Mining Introductory and Advanced Topics by Margaret H Dunham
The Student Edition of The Data Warehouse Lifecycle Toolkit by Ralph Kimball
Data Warehousing in the Real World by Dennis Murray and Sam Anahory
Book on Data Mining- A Practical Machine Learning Tools and Techniques by Ian H. Witten, Mark A. Hall, and Eibe Frank
Book on Data Mining- Concepts and Techniques by Vikram Pudi
Data Mining: Concepts and Techniques by Jiawei Han, Jian Pei Professor, and Micheline Kamber
Data Warehousing, Data Mining, and Olap by Alex Berson and Stephen Smith
The First Edition of Data Mining- Introductory and Advanced Topics by Dunham
Data Mining- Concepts, Methods and Applications in Management and Engineering Design by Jiafu Tang, Yong Yin, JianMing Zhu, and Ikou Kaku
The Second Edition of Data Mining and Predictive Analytics by Daniel T. Larose and Chantal D. Larose
Introduction to Data Mining by Pang-Ning Tan, Vipin Kumar, and Micheal Steinbach
Database Systems- Introduction to Databases and Data Warehouses by Nenad Jukic, Susan Vrbsky, and Svetlozar Nestorov
The First Edition of Building a Data Warehouse- With Examples in SQL Server by Vincent Rainardi

Data Mining and Data Warehousing Curriculum

An effective way to make your preparation better has an initial idea, and a basic overview of the Data Mining And Data Warehousing updated Syllabus. The article provides a detailed and concise view of the Data Mining And Data Warehousing curriculum, keeping in mind of every student’s requirements.

Course Curriculum is an important course plan that gives students a clear idea of what to study and how to study. The article provides unit-wise segregation of all the important topics under each unit carefully and can allot time to each topic accordingly.

Students should ensure to cover all the topics before attempting the Data Mining And Data Warehousing exam so that the paper is reasonably answerable at the time of the exam. Also, the Data Mining And Data Warehousing updated syllabus ensures awareness of the essential topics and prevents you from squandering unnecessary time on redundant topics.

The updated unit-wise breakup of the Data Mining And Data Warehousing Syllabus is as follows-

Unit	Topics
UNIT-I Introduction	What is Data Mining The Use Of Data mining The Kinds of Data that can be mined The Kinds of Pattern that can be Mined Targeted Applications Technologies Used Measuring Data Similarity and Dissimilarity Data Objects and Attribute Types Data Visualisation Major Issues in Data Mining Basic Statistical Descriptions of Data
UNIT-II Data Pre-processing	An Overview of Data Preprocessing Data Reduction Data Discretisation Data Integration Data Cleaning Data Transformation
UNIT-III Classification	Basic Concepts Decision Tree Induction- Algorithm for Decision Tree Induction, The Working of Decision Tree, Methods for Expressing Attribute Test Conditions, Building a Decision Tree, and Measures for Selecting the best Split Approaches to Solve a Classification Problem
UNIT-IV Classification	Naïve Bayesian Classification Alternative Techniques Bayesian Belief Networks Bayes’ Theorem
UNIT-V Association Analysis	Basic Concepts and Algorithms Frequent Item Set Generation FP-Growth Algorithm Compact Representation of Frequent Itemsets Problem Defecation Rule generation
UNIT-VI Cluster Analysis	Basic Concepts and Algorithms An Overview of Cluster Analysis- Types of Clustering, Definition of Clustering Analysis, and Different Types of Clusters Agglomerative Hierarchical Clustering- basics of Agglomerative Hierarchical Clustering Algorithm DBSCAN- DBSCAN Algorithm, Weakness, Traditional Density Center-Based Approach, and Strengths K-means- Bisecting K-means, Basics of K-means, Strengths, K-means Additional Issues, and Weakness

List of Data Mining and Data Warehousing Important Questions

Candidates pursuing Bachelors in Technology (B.Tech) can go through the list of essential questions mentioned below for the Data Mining And Data Warehousing course programme. All the given review questions aim to help the candidates to excel in the examination.

Define Data Partitioning and Metadata with examples
Enlist and explain in detail the Alternate Technologies that improve Data Warehouse Environment.
Give a brief analysis on Data Transformation and Data Integration.
Define Clustering and Regression with suitable examples.
Differentiate between Supervised learning and Unsupervised learning with suitables examples.
Describe the Applications of learning in Data warehousing.
Illustrate and Explain the Architecture for On-line Analytical Mining.
Write the syntax for the following data mining primitives

Task Relevant Data
Concept Hierarchies

Demonstrate the following-

Data Warehouse Management and Administration
Data marts

The contrast between the Query Tools and Data Mining Tools
Define OLTP and OLAP
Write a short note on the significance and performance of Analytical Characterisation.
Illustrate with an example and discuss the constraints based Mining.
Describe in detail about Data Transformation and Data Extraction
Elucidate the Implementation of a Data Warehousing.

FAQs on Data Mining And Data Warehousing Lecture Notes Free Download

Question 1.
Define Data Warehousing and Data Mining with examples.

Answer:
Data Mining is a process that analyses different data patterns and also extracts data from large data sets. Data Warehousing is a database system that designs analytical data over transactional data. This process pools all relevant data.

Question 2.
Name the different components of Data Mining.

Answer:
Data mining holds four major components-

Pattern-Evaluation Module
Knowledge Base
User-Interface
Data Mining Engine

Question 3.
What are the various concepts involved in the Data Preprocessing module?

Answer:
The essential concepts are- An Overview of Data Preprocessing, Data Reduction, Data Discretisation, Data Integration, Data Cleaning, and Data Transformation.

Question 4.
Name a few important books for Data Mining And Data Warehousing course preparation.

Answer:

Data Warehousing in the Real World by Dennis Murray and Sam Anahory
Book on Data Mining- A Practical Machine Learning Tools and Techniques by Ian H. Witten, Mark A. Hall, and Eibe Frank
Book on Data Mining- Concepts and Techniques by Vikram Pudi
Data Mining: Concepts and Techniques by Jiawei Han, Jian Pei Professor, and Micheline Kamber
Data Warehousing, Data Mining, and Olap by Alex Berson and Stephen Smith

Conclusion

The article piece on Data Mining And Data Warehousing Lecture Note Free Download is credible and accurate and all the Study mentioned above Materials and Books foster help and improve every student’s knowledge and apprehension of the subject during preparations and at the time of examination. Students can avail and download the materials on Data Mining, And Data Warehousing Lecture Notes Free Download.