Data Mining Lecture Notes Pdf Download- B.Tech 3rd year Study Material, Lecture Notes, Books

Data Mining Lecture Notes: Check Out Data Mining Lecture Notes Pdf Download.  You can get the Complete Notes on Data Mining in a Single Download Link for B.Tech Students. Data Mining Study Materials, Important Questions List, Data Mining Syllabus, Data Mining Lecture Notes can be download in Pdf format. We provide B.tech Data Mining study materials (डाटा माइनिंग लेक्चर नोट्स) to B.Tech  student with free of cost and it can download easily and without registration need.

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Data Mining Lecture Notes Pdf Download

What Is Data Mining? Data mining refers to extracting or mining knowledge from large amounts of data. The term is actually a misnomer. Thus, data mining should have been more appropriately named as knowledge mining which emphasis on mining from large amounts of data. It is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use

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List of Reference Books for Data Mining- B.Tech 3rd Year

  • Introduction to Data Mining: Pang-Ning Tan & Michael Steinbach, Vipin Kumar, Pearson.
  • Data Mining concepts and Techniques, 3/e, Jiawei Han, Michel Kamber, Elsevier.
  • The  Data Mining Techniques and Applications: An Introduction, Hongbo Du, Cengage Learning.
  • Data Mining : Vikram Pudi and P. Radha Krishna, Oxford.
  • Data Mining and Analysis – Fundamental Concepts and Algorithms; Mohammed J. Zaki, Wagner Meira, Jr, Oxford
  • Data Warehousing Data Mining & OLAP, Alex Berson, Stephen Smith, TMH.

Data mining Syllabus for B.Tech 3rd Year

OBJECTIVES:
• 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.

UNIT –I:

Introduction: Why Data Mining? What Is Data Mining?1.3 What Kinds of Data Can Be Mined?1.4 What Kinds of Patterns Can Be Mined? Which Technologies Are Used? Which Kinds of Applications Are Targeted? Major Issues in Data Mining. Data Objects and Attribute Types, Basic Statistical Descriptions of Data, Data Visualization, Measuring Data Similarity and Dissimilarity

UNIT –II:

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

UNIT –III:

Classification: Basic Concepts, General Approach to solving a classification problem, Decision Tree Induction: Working of Decision Tree, building a decision tree, methods for expressing attribute test conditions, measures for selecting the best split, Algorithm for decision tree induction.

UNIT –IV:

Classification: Alternative Techniques, Bayes’ Theorem, Naïve Bayesian Classification, Bayesian Belief Networks

UNIT –V:

Association Analysis: Basic Concepts and Algorithms: Problem Defecation, Frequent Item Set generation, Rule generation, compact representation of frequent item sets, FP-Growth Algorithm. (Tan & Vipin)

UNIT –VI:

Cluster Analysis: Basic Concepts and Algorithms: Overview: What Is Cluster Analysis? Different Types of Clustering, 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)

OUTCOMES:
• Understand stages in building a Data Warehouse
• Understand the need and importance of preprocessing techniques
• Understand the need and importance of Similarity and dissimilarity techniques
• Analyze and evaluate the performance of algorithms for Association Rules.
• Analyze Classification and Clustering algorithms

Data Mining Books Buy Online at Amazon

Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems)
  • Morgan Kaufmann Publishers
  • Jiawei Han, Micheline Kamber, Jian Pei Professor
  • Morgan Kaufmann
  • Edition no. 3 (07/25/2011)
  • Hardcover: 744 pages
Sale
Data Mining: Practical Machine Learning Tools and Techniques
  • Ian H. Witten, Eibe Frank, Mark A. Hall
  • Elsevier
  • Edition no. Third (01/01/1970)
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Data Mining: Introductory and Advanced Topics, 1e
  • DUNHAM
  • Pearson Education India
  • Edition no. 1 (01/01/1970)
  • Paperback: 328 pages
Data Mining: Concepts and Techniques
  • Vikram Pudi
  • Oxford University Press
  • Edition no. Error (01/12/2009)
  • Paperback: 512 pages
Sale
DATA WAREHOUSING, DATA MINING, & OLAP
  • Alex Berson, Stephen Smith
  • McGraw Hill Education
  • Edition no. 1st (07/01/2017)
  • Paperback: 638 pages
Sale
Data Mining and Predictive Analytics, 2ed (MISL-WILEY)
  • Daniel T. Larose, Chantal D. Larose
  • Wiley
  • Edition no. Second (01/01/1970)
  • Paperback: 824 pages
Sale
Data Mining: Concepts, Methods and Applications in Management and Engineering Design (Decision Engineering)
  • Springer
  • Yong Yin, Ikou Kaku, Jiafu Tang, JianMing Zhu
  • Springer
  • Edition no. 2011 (11/26/2014)
  • Paperback: 312 pages

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  1. Hemprakash Tandon says

    Plz all mining study notes share by my emails

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