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JNTUK R20 3-1 Data Warehousing and Data Mining Material PDF Download

Students those who are studying JNTUK R20 CSE Branch, Can Download Unit wise R20 3-1 Data Warehousing and Data Mining (DW&DM) Material/Notes PDFs below.

Course Objectives: The main objectives are

  • Introduce basic concepts and techniques of data warehousing and data mining
  • Examine the types of the data to be mined and apply pre-processing methods on raw data
  • Discover interesting patterns, analyze supervised and unsupervised models and estimate the accuracy of the algorithms.

Course Outcomes: At the end of the course, the students will be able to:

  • Illustrate the importance of Data Warehousing, Data Mining and its functionalities and Design schema for real time data warehousing applications.
  • Demonstrate on various Data Preprocessing Techniques viz. data cleaning, data integration, data transformation and data reduction and Process raw data to make it suitable for various data mining algorithms.
  • Choose appropriate classification technique to perform classification, model building and evaluation.
  • Make use of association rule mining techniques viz. Apriori and FP Growth algorithms and analyze on frequent itemsets generation.
  • Identify and apply various clustering algorithm (with open source tools), interpret, evaluate and report the result.
  • UNIT-1

    Data Warehousing and Online Analytical Processing: Data Warehouse: Basic concepts, Data Warehouse Modelling: Data Cube and OLAP, Data Warehouse Design and Usage, Data Warehouse Implementation, Introduction: Why and What is data mining, What kinds of data need to be mined and patterns can be mined, Which technologies are used, Which kinds of applications are targeted.

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  • UNIT-2

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

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Classification: Basic Concepts, General Approach to solving a classification problem, Decision Tree Induction: Attribute Selection Measures, Tree Pruning, Scalability and Decision Tree Induction, Visual Mining for Decision Tree Induction.

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Association Analysis: Problem Definition, Frequent Item set Generation, Rule Generation: Confident Based Pruning, Rule Generation in Apriori Algorithm, Compact Representation of frequent item sets, FPGrowth Algorithm.

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Cluster Analysis: Overview, Basics and Importance of Cluster Analysis, Clustering techniques, Different Types of Clusters; K-means: The Basic K-means Algorithm, K-means Additional Issues, Bi-secting K Means

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