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B.Tech 3rd Year Data Mining Study Materials BOOK PDF 2020|Download B.Tech 3rd Year Data Mining Study Materials BOOK PDF

Here, we have provided the links containing the study materials, which will help you study and prepare for your B.Tech 3rd Year Data Mining 2020 edition examinations. Referring to the connections we’ve provided below and the links containing the study materials in PDF format, and the list of recommended books that we’ve provided below, you will be able to ace your examinations. We have also provided you with further details that will allow you to do well in your exams and learn more. These study materials help you understand the concepts and everything quickly and creates a better space for you to work on. These study materials give you the best resources to study from.

Download Data Mining Study Materials

Data Mining Notes 7th semDownload
Data Mining Notes  for StudentsDownload
Data Mining Lecture NotesDownload
Data Mining Notes PPTDownload

Recommended Books

  • 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.



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


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


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.


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


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)


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)

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