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JNTU-K B.TECH R-19 IT 3-2 Syllabus For Data warehousing and data mining PDF 2022

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JNTU-K B.TECH R-19 IT 3-2 Syllabus For Data warehousing and data mining PDF 2022

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You will be able to find information about Data warehousing and data mining along with its Course Objectives and Course outcomes and also a list of textbook and reference books in this blog.You will get to learn a lot of new stuff and resolve a lot of questions you may have regarding Data warehousing and data mining after reading this blog. Data warehousing and data mining has 5 units altogether and you will be able to find notes for every unit on the CynoHub app. Data warehousing and data mining can be learnt easily as long as you have a well planned study schedule and practice all the previous question papers, which are also available on the CynoHub app.

All of the Topic and subtopics related to Data warehousing and data mining are mentioned below in detail. If you are having a hard time understanding Data warehousing and data mining or any other Engineering Subject of any semester or year then please watch the video lectures on the official CynoHub app as it has detailed explanations of each and every topic making your engineering experience easy and fun.

Data warehousing and data mining Unit One

UNIT 1

Data Warehousing, Business Analysis and On-Line Analytical Processing (OLAP):Basic Concepts, Data Warehousing Components, Building a Data Warehouse, Database Architectures for Parallel Processing, Parallel DBMS Vendors, Multidimensional Data Model, Data Warehouse Schemas for Decision Support, Concept Hierarchies, Characteristics of OLAP Systems, Typical OLAP Operations, OLAP and OLTP.

Data warehousing and data mining Unit Two

UNIT 2

Introduction to Data Mining Systems, Knowledge Discovery Process, Data Mining Techniques, Issues, applications, Data Objects and attribute types, Statistical description of data, Data Preprocessing –Cleaning, Integration, Reduction, Transformation and discretization, Data Visualization, Data similarity and dissimilarity measures.

Get Complete Lecture Notes for Data warehousing and data mining on Cynohub APP

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Data warehousing and data mining Unit Three

UNIT 3

Frequent Pattern Analysis: Mining Frequent Patterns, Associations and Correlations, Mining Methods, Pattern Evaluation Method, Pattern Mining in Multilevel, Multi-Dimensional Space –Constraint Based Frequent Pattern Mining, Classification using Frequent Patterns

Data warehousing and data mining Unit Four

UNIT 4

Classification:Decision Tree Induction, Bayesian Classification, Rule Based Classification, Classification by Back Propagation, Support Vector Machines, Lazy Learners, Model Evaluation and Selection, Techniques to improve Classification Accuracy

Data warehousing and data mining Unit Five

UNIT 5

Clustering:Clustering Techniques, Cluster analysis, Partitioning Methods, Hierarchical methods, Density Based Methods, Grid Based Methods, Evaluation of clustering, Clustering high dimensional data, Clustering with constraints, Outlier analysis, outlier detection methods.

Data warehousing and data mining Course Objectives

To understand data warehouse concepts, architecture, business analysis and tools To understand data pre-processing and data visualization techniques To study algorithms for finding hidden and interesting patterns in data To understand and apply various classification and clustering techniques using tools

Data warehousing and data mining Course Outcomes

At the end of the course, the students will be able to:Design a Data warehouse system and perform business analysis with OLAP toolsApply suitable pre-processing and visualization techniques for data analysis Apply frequent pattern and association rule mining techniques for data analysisApply appropriate classification techniques for data analysisApply appropriate clustering techniques for data analysis

Data warehousing and data mining Text Books

1)Jiawei Han and MichelineKamber, “Data Mining Concepts and Techniques”, Third Edition, Elsevier, 2012.2)Pang-NingTan, Michael Steinbach and Vipin Kumar, Introductionto Data Mining, Pearson,2016.

Data warehousing and data mining Reference Books

1)Alex Berson and Stephen J.Smith, ―Data Warehousing, Data Mining & OLAP‖, Tata McGraw –Hill Edition, 35th Reprint 2016. 2)K.P. Soman, ShyamDiwakar and V. Ajay, ―Insight into Data Mining Theory and Practice‖, Eastern Economy Edition, Prentice Hall of India, 2006.3)Ian H.Witten and Eibe Frank, ―Data Mining: Practical Machine Learning Tools and Techniques‖, Elsevier, Second Edition.

Scoring Marks in Data warehousing and data mining

Scoring a really good grade in Data warehousing and data mining is a difficult task indeed and CynoHub is here to help!. Please watch the video below and find out how to get 1st rank in your B.tech examinations . This video will also inform students on how to score high grades in Data warehousing and data mining. There are a lot of reasons for getting a bad score in your Data warehousing and data mining exam and this video will help you rectify your mistakes and help you improve your grades.

Information about JNTU-K B.Tech R-19 Data warehousing and data mining was provided in detail in this article. To know more about the syllabus of other Engineering Subjects of JNTUH check out the official CynoHub application. Click below to download the CynoHub application.

Get Complete Lecture Notes for Data warehousing and data mining on Cynohub APP

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