CS-703-B Data Mining and Warehousing - Dec 2024 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
   aExplain about the Three-tier data warehouse architecture with a neat diagram.
   How to answer

   • Define three-tier architecture: bottom, middle, top tiers
   • Explain function of each tier: warehouse server, OLAP server, client tools
   • Describe data flow from sources to clients
   • Provide neat labelled diagram
2. 7 Marks
   bDiscuss the various types of warehouse schema with suitable example.
   How to answer

   • Define warehouse schema concept
   • Explain star schema with example
   • Explain snowflake schema with example
   • Explain fact constellation/galaxy schema with example
3. 7 Marks
   aEnumerate the building blocks of a data warehouse. Explain the importance of metadata in a data warehouse environment. What are the challenges in metadata management?
   How to answer

   • List building blocks: source data, staging, storage, metadata, management, access tools
   • Explain importance/role of metadata
   • Types of metadata
   • Challenges in metadata management
4. 7 Marks
   bExplain the role played by sourcing, acquisition, clean up and transformation tools in data warehousing.
   How to answer

   • Define ETL sourcing and acquisition
   • Explain data cleanup need and tasks
   • Explain transformation operations
   • Role of tools in populating warehouse
5. 7 Marks
   aWrite the difference between multi-dimensional OLAP (MOLAP) and Multi-relational OLAP (ROLAP).
   How to answer

   • Define MOLAP and ROLAP
   • Compare storage, performance, scalability, query handling
   • Give advantages/limitations of each
   • Tabular difference summary
6. 7 Marks
   bExplain about how to use OLAP tools on the Internet.
   How to answer

   • Define web-enabled OLAP / Internet OLAP
   • Explain architecture for OLAP on Internet
   • Steps to access and use OLAP tools via browser
   • Examples and benefits
7. 7 Marks
   aExplain the following in OLAP :

   1. i)Roll up operation
   2. ii)Drill down operation
   3. iii)Slice operation
   4. iv)Dice operation
   How to answer

   • Define OLAP cube and dimensions
   • Explain roll-up vs drill-down with example
   • Explain slice vs dice with example
   • Illustrate operations on data cube
8. 7 Marks
   bExplain various methods of data cleaning in detail.
   How to answer

   • Define data cleaning need
   • Explain handling missing values
   • Explain noisy data and outlier handling
   • Explain inconsistency resolution and methods
9. 7 Marks
   aExplain with diagrammatic illustration the primitives for specifying a data mining task.
   How to answer

   • Define data mining task primitives
   • List five primitives: task-relevant data, knowledge type, background knowledge, interestingness, presentation
   • Explain each primitive
   • Provide diagrammatic illustration
10. 7 Marks
    bExplain the various data mining issues and functionalities in detail.
    How to answer

    • Define data mining functionalities: characterization, association, classification etc.
    • Explain major issues: methodology, performance, diversity
    • Discuss user interaction and data source issues
    • Give examples
11. 7 Marks
    aExplain various learning techniques involved in supervised learning.
    How to answer

    • Define supervised learning
    • Explain decision trees, Naive Bayes, SVM, neural networks, k-NN
    • Compare techniques and training process
    • Give applications
12. 7 Marks
    bExplain distance-based algorithms.
    How to answer

    • Define distance-based methods and distance metrics
    • Explain k-nearest neighbours algorithm steps
    • Explain k-means / case-based reasoning if applicable
    • Discuss advantages and limitations
13. 7 Marks
    aExplain Decision tree-based algorithms.
    How to answer

    • Define decision tree and components
    • Explain splitting criteria: entropy, information gain, Gini
    • Explain tree induction, pruning steps
    • Give example
14. 7 Marks
    bExplain about data mining applications.
    How to answer

    • Define data mining applications scope
    • Explain applications in retail, banking, healthcare, telecom
    • Explain fraud detection, market basket analysis
    • Discuss benefits
15. 7 Marks
    aDescribe in detail about types of data in cluster analysis.
    How to answer

    • Define cluster analysis and data types
    • Explain interval-scaled, binary, categorical variables
    • Explain ordinal and ratio-scaled variables
    • Explain mixed-type data handling
16. 7 Marks
    bExplain the following clustering methods in detail.

    1. i)BIRCH
    2. ii)CURE
    How to answer

    • Define hierarchical clustering context
    • Explain BIRCH: CF-tree, phases, features
    • Explain CURE: representative points, shrinking, merging
    • Compare suitability for large databases