Data Mining: You can download the Study materials and notes for Data Mining in PDF files from the official website.
Data Mining Books
We inform the third year B-tech students that the notes and Study materials for Data Mining are now available on the official website. The notes and Study materials are available as download links in pdf format. Candidates can easily download the notes and start preparing for the exam. Data mining software allows organizations to examine data from several sources to detect patterns.
Data Mining Books 2018
Data mining is the method of sorting through large data sets to recognize patterns and build relationships to solve problems through data analysis. Data mining tools allow enterprises to predict future trends. We have gathered the best authors and their books so that candidate does not need to go in search of the books. Also, we are providing Short study notes which can be a life savior for those who cannot completely cover the whole book. This notes can be beneficial for you on the go. There will be no barriers, and you can prepare from anyplace any time you want.
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 itemsets, 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 Lecture Notes Pdf Download
| Data Mining Notes 7th sem | Download |
| Data Mining Notes for Students | Download |
| Data Mining Lecture Notes | Download |
| Data Mining Notes PPT | Download |
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.
We have provided you with all the necessary books and study materials. So without any further delay candidates can download the notes in pdf format and start preparing for their examination. Make sure that you share this link with your friends so that, this can be helpful for them also.
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