DATA WARE HOUSING AND DATA MINING - R20- III Year –I Semester - JNTUK

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The Data Warehousing stage involves collecting data, organizing it, transforming it into a standard structure, optimizing it for analysis, and processing it.

The Data Mining stage involves analyzing data to discover unknown patterns, relationships, and insights.

UNIT WISE Importent Questions

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click here Unitwise Questions with Answers

click here Objective type Questions

Problems Solutions of DWDM

click here Dwdm Problems Solutions

click here Apiori and FP growth Problems    

click here K-means Problem Solution

A database has four transactions. Let min_sup=60%  and min_conf=80%
TID                 Date                     Items bought
100                 10/15/2018         {K, A, B, D}
200                10/15/2018         {D, A, C, E, B}
300                 10/19/2018         {C, A, B, E}
400                 10/22/2018         {B, A, D}
i) Find all frequent items using Apriori & FP-growth,respectively. 

Compare the efficiency of the two meaning process.
ii)List all of the strong association rules.

click here apiori problem Solution 

click here FP growth problem 

DWDM Lab Manual

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UNIT –I:

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 –II:

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

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UNIT –III:

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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UNIT –IV:

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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UNIT –V

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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