Data Warehousing and Mining Notes, PDF I MBA 2021

  • Post last modified:9 April 2021
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Download Data Warehousing and Mining Notes, PDF, Books, Syllabus for MBA 2021. We provide complete Data Warehousing and Mining pdf. Data Warehousing and Mining study material includes Data Warehousing and Mining notes, book, courses, case study, syllabus, question paper, MCQ, questions and answers and available in Data Warehousing and Mining pdf form.

Data Warehousing and Mining subject is included in MBA so students are able to download Data Warehousing and Mining notes for MBA 3rd year and Data Warehousing and Mining notes for MBA 5th semester.

Data Warehousing and Mining Notes can be downloaded in Data Warehousing and Mining pdf from the below article.


Data Warehousing and Mining Syllabus

A detailed Data Warehousing and Mining syllabus as prescribed by various Universities and colleges in India are as under. You can download the syllabus in Data Warehousing and Mining pdf form.

  • UNIT-I Data warehouse: Introduction to Data warehouse, Difference between operational database systems and data warehouses, Data warehouse Characteristics, Data warehouse Architecture and its Components, Extraction-Transformation-Loading, Logical(Multi-Dimensional), Data Modeling, Schema Design, Star and Snow-Flake Schema, Fact Constellation, Fact Table, Fully Addictive, Semi-Addictive, Non-Addictive Measures; Fact-Less-Facts, Dimension Table Characteristics; OLAP Cube, OLAP Operations, OLAP Server Architecture-ROLAP, MOLAP and HOLAP.

  • UNIT-II Introduction: Fundamentals of data mining, Data Mining Functionalities, Classification of Data Mining systems, Data Mining Task Primitives, Integration of a Data Mining System with a Database or Data Warehouse System, Major issues in Data Mining. Data Preprocessing: Need for Preprocessing the Data, Data Cleaning, Data Integration &Transformation, Data Reduction, Discretization and Concept Hierarchy Generation.

  • UNIT-III Association Rules: Problem Definition, Frequent Item Set Generation, The APRIORI Principle, Support and Confidence Measures, Association Rule Generation; APRIOIRI Algorithm, The Partition Algorithms, FP-Growth Algorithms, Compact Representation of Frequent Item Set- Maximal Frequent Item Set, Closed Frequent Item Set.

  • UNIT-IV Classification: Problem Definition, General Approaches to solving a classification problem, Evaluation of Classifiers , Classification techniques, Decision Trees-Decision tree Construction, Methods for Expressing attribute test conditions, Measures for Selecting the Best Split, Algorithm for Decision tree Induction; Naive-Bayes Classifier, Bayesian Belief Networks; K- Nearest neighbor classification-Algorithm and Characteristics, prediction: Accuracy and Error measures, Evaluating the accuracy of a classifier or a predictor, Ensemble methods.

  • UNIT-V Clustering: Clustering Overview, A Categorization of Major Clustering Methods, partitioning methods, hierarchical methods, , partitioning clustering-k-means algorithm, pam algorithm; hierarchical clustering-agglomerative methods and divisive methods, Basic Agglomerative Hierarchical Clustering Algorithm, Key Issues in Hierarchical Clustering, Strengths and Weakness,Outlier Detection.

Data Warehousing and Mining PDF

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Data Warehousing and Mining Notes

Knowledge Discovery in Databases(KDD): Some people treat data mining same as Knowledge discovery while some people view data mining essential step in process of knowledge discovery.

Generic Category (English)300x250

Data Mining is defined as extracting information from huge sets of data. In other words, we can say that data mining is the procedure of mining knowledge from data.

A data warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management’s decision making process.


Data Warehousing and Mining Questions and Answers

If you have already studied the Data Warehousing and Mining and notes, then it’s time to move ahead and go through previous year Data Warehousing and Mining question and answers.

  1. Explain different data mining tasks.
  2. What is the relation between data warehousing and data mining?
  3. Explain the differences between “Explorative Data Mining” and “Predictive Data Mining” and give one example of each.
  4. What are the application areas of Data Mining?
  5. Explain the differences between Knowledge discovery and data mining.
  6. How is a data warehouse different from a database? How are they similar?
  7. What type of benefit you might hope to get from data mining?
  8. What are the key issues in Data Mining?
  9. How can Data Mining help business analysts?
  10. What are the limitations of data Mining?
  11. Discuss the need of human intervention in data mining process.

Data Warehousing and Mining Question Paper

If you have already studied the Data Warehousing and Mining and notes, then it’s time to move ahead and go through previous year Data Warehousing and Mining question paper.

It will help you to understand the question paper pattern and type of Data Warehousing and Mining question and answer asked in MBA 3rd year Data Warehousing and Mining exam. You can download the syllabus in Data Warehousing and Mining pdf form.


Data Warehousing and Mining Books

Below is the list of Data Warehousing and Mining books recommended by the top university in India.

  • Data Mining Techniques, Arun KPujari, 3rd Edition, Universities Press.
  • Data Warehousing Fundament’s, Pualraj Ponnaiah, Wiley Student Edition.
  • The Data Warehouse Life CycleToolkit — Ralph Kimball, Wiley Student Edition.
  • Data Mining, Vikaram Pudi, P Radha Krishna, Oxford University Press.

In the above article, a student can download Data Warehousing and Mining notes for MBA 3rd year and Data Warehousing and Mining notes for MBA 6th semester. Data Warehousing and Mining study material include Data Warehousing and Mining notes, Data Warehousing and Mining books, Data Warehousing and Mining syllabus, Data Warehousing and Mining question paper, Data Warehousing and Mining case study, Data Warehousing and Mining questions and answers, Data Warehousing and Mining courses in Data Warehousing and Mining pdf form.

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