AD-603-A Data Mining and Warehousing - May 2023 Question Paper

Under each question, How to answer lists the points an answer should cover, outlined by CampusPrep from the syllabus. Check the details against your textbook.

1. 7 Marks
   aDescribe 3-tier Architecture of Data Warehouse with a neat sketch.
   How to answer

   • Define 3-tier architecture: bottom, middle, top tiers
   • Describe each tier - warehouse DB, OLAP server, client tools
   • Neat labelled sketch showing data sources to tiers
2. 7 Marks
   bDescribe the various phases in knowledge discovery process with a neat diagram.
   How to answer

   • Define KDD / knowledge discovery process
   • List phases: selection, preprocessing, transformation, mining, evaluation
   • Neat diagram showing iterative sequence of phases
3. 7 Marks
   a"Data preprocessing is necessary before data mining process" Justify your answer.
   How to answer

   • Define data preprocessing need for quality mining
   • Explain cleaning, integration, reduction, transformation
   • Justify with effects of noisy, incomplete, inconsistent data
4. 7 Marks
   bDifferentiate ROLAP, MOLAP and HOLAP server functionalities.
   How to answer

   • Define ROLAP, MOLAP, HOLAP
   • Compare storage, scalability, performance, architecture
   • Give suitable examples/use cases
5. 7 Marks
   aDescribe various OLAP operations performed on Multidimensional Data Model.
   How to answer

   • Define multidimensional data model and OLAP
   • Describe roll-up, drill-down, slice, dice, pivot
   • Give example with data cube
6. 7 Marks
   bCompare and contrast online transaction processing with online analytical processing.
   How to answer

   • Define OLTP and OLAP
   • Compare on features: orientation, users, data, queries
   • Summarize in tabular form
7. 7 Marks
   aExplain about various Data Mining Tasks with appropriate examples.
   How to answer

   • Define data mining tasks: predictive vs descriptive
   • Explain classification, clustering, association, etc.
   • Give appropriate example for each task
8. 7 Marks
   bDiscuss about any two measures of similarity.
   How to answer

   • Define similarity/distance measures
   • Explain any two e.g. Euclidean, cosine, Jaccard
   • Give formula and use case
9. 7 Marks
   aDiscuss about issues in data mining.
   How to answer

   • List major issues: mining methodology, performance, diversity
   • Discuss privacy, data quality, scalability issues
   • Conclude with challenges
10. 7 Marks
    bWhy is Naive Bayesian classification called "Naive"? Briefly outline the major ideas of Naive Bayesian classification. Explain Naive-Bayes classification.
    How to answer

    • Explain Bayes theorem and naive independence assumption
    • Outline major ideas: class conditional independence
    • Explain steps of Naive-Bayes classification with example
11. 7 Marks
    aExplain Decision tree induction algorithm for classification. Discuss the usage of information gain in this.
    How to answer

    • Describe decision tree induction algorithm steps
    • Define entropy, information gain, gain ratio
    • Explain attribute selection with formula and example
12. 7 Marks
    bDiscuss about neural network based Algorithms.
    How to answer

    • Define artificial neural network structure
    • Explain learning/backpropagation for classification
    • Mention advantages and applications
13. 7 Marks
    aWhat are key issues in hierarchical clustering? Explain.
    How to answer

    • Define hierarchical clustering types
    • Explain key issues: scalability, linkage, termination
    • Suggest handling of issues
14. 7 Marks
    bIllustrate FP-growth algorithm with a suitable example.
    How to answer

    • Define frequent pattern mining and FP-tree
    • Explain FP-growth steps: tree construction, mining
    • Illustrate with suitable transactional example
15. 14 Marks
    Write short notes on any two:

    1. a)Maximal frequent item set
    2. b)Basic data mining tasks
    3. c)Pattern warehousing
    4. d)DBSCAN algorithm
    How to answer

    • Select any two topics from list
    • Define each with key concepts and example
    • Give applications/limitations briefly