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JNTU-K B.TECH R-19 III Year-II Semester Syllabus For Data mining PDF 2022

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JNTU-K B.TECH R-19 III Year-II Semester Syllabus For Data mining PDF 2022

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Get Complete Lecture Notes for Data mining on Cynohub APP

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You will be able to find information about 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 mining after reading this blog. Data mining has 5 units altogether and you will be able to find notes for every unit on the CynoHub app. 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 mining are mentioned below in detail. If you are having a hard time understanding 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 mining Unit One

INTRODUCTION

Need of Data Warehouse, Need and Usage of Data Mining Technologies, Types of Data and Patterns to be mined, In Real Time Applications. Brief Introduction of Pattern Recognition: Pattern, Feature, Database Query Vs Mining, Curse of Dimensionality, Need for Efficiency. Major Issues in Data Mining. Data Objects and Attribute Types, Basic Statistical Descriptions of Data, Data Visualization, Measuring Data Similarity and Dissimilarity

Data mining Unit Two

DATA PRE-PROCESSING

Data Preprocessing: An Overview, Data Cleaning, Data Integration, Data Reduction, Data Transformation and Data Discretization

Get Complete Lecture Notes for Data mining on Cynohub APP

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Data mining Unit Three

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. Bayes’ Theorem, Naïve Bayesian Classification, Bayesian Belief Networks

Data mining Unit Four

ASSOCIATION ANALYSIS: BASIC CONCEPTS ANDALGORITHMS

Problem Defecation, Frequent Item Set generation, Rule generation, compact representation of frequent item sets, FP-Growth Algorithm. (Tan &Vipin)

Data mining Unit Five

CLUSTER ANALYSIS: BASIC CONCEPTS AND ALGORITHMS

Basics and Importance of Cluster Analysis, Clustering techniques, 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)

Data mining Course Objectives

The main objectives of this course are:
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.

Data mining Course Outcomes

At the end of this course the student will be able to:
Understand Data Mining Principles
Identify appropriate data mining algorithms to solve real world problems
Compare and evaluate different data mining techniques like classification, prediction, clustering and association rule mining

Data mining Text Books

1. Introduction to Data Mining: Pang-Ning Tan & Michael Steinbach, Vipin Kumar, Pearson.
2. Data Mining concepts and Techniques, 3/e, Jiawei Han, Michel Kamber, Elsevier.

Data mining Reference Books

1.Data Mining Techniques and Applications: An Introduction, Hongbo Du, Cengage Learning.
2.Data Mining: VikramPudi and P. Radha Krishna, Oxford.
3.Data Mining and Analysis -Fundamental Concepts and Algorithms; Mohammed J. Zaki, Wagner Meira, Jr, Oxford
4.Data Warehousing Data Mining & OLAP, Alex Berson, Stephen Smith, TMH.
5.http://onlinecourses.nptel.ac.in/noc18_cs14/preview(NPTEL course by Prof.Pabitra Mitra)
6.http://onlinecourses.nptel.ac.in/noc17_mg24/preview(NPTEL course by Dr. Nandan Sudarshanam& Dr. Balaraman Ravindran)http://www.saedsayad.com/data_mining_map.htm

Scoring Marks in Data mining

Scoring a really good grade in 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 mining. There are a lot of reasons for getting a bad score in your 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 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 mining on Cynohub APP

Download the APP Now! ( Click Here )

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