CS-703-B Data Mining and Warehousing - Jun 2025 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
   aGive detailed information about Meta data in data warehousing.
   How to answer

   • Define metadata in data warehousing context
   • Types: operational, extraction, end-user metadata
   • Role in ETL, query processing and administration
   • Example repository contents
2. 7 Marks
   bExplain the role played by sourcing, acquisition, clean up and transformation tools in data warehousing.
   How to answer

   • Define sourcing, acquisition, clean-up, transformation
   • Role of ETL tools in extraction and integration
   • Data cleaning and transformation steps
   • Impact on data quality and warehouse usability
3. 7 Marks
   aWrite in brief about schemas in multidimensional data model.
   How to answer

   • Define multidimensional data model
   • Star schema structure and features
   • Snowflake and fact constellation schemas
   • Comparison and use cases with diagram
4. 7 Marks
   bDescribe in brief about data warehouse implementation.
   How to answer

   • Define data warehouse implementation phases
   • ETL, data modeling and storage steps
   • OLAP and front-end tools
   • Testing, deployment and maintenance
5. 7 Marks
   aWrite the difference between multi-dimensional OLAP (MOLAP) and Multi-relational OLAP (ROLAP).
   How to answer

   • Define MOLAP and ROLAP
   • Storage, performance and scalability differences
   • Tabular comparison on architecture and use
   • Examples of MOLAP vs ROLAP tools
6. 7 Marks
   bDiscuss in detail about the case study of Data warehouse for the Government of Madhya Pradesh.
   How to answer

   • Objective of warehouse for Govt. of Madhya Pradesh
   • Data sources and schema design
   • ETL and implementation aspects
   • Benefits and decision support uses
7. 7 Marks
   aWhat is KDD? Explain about data mining as a step in the process of knowledge discovery.
   How to answer

   • Define KDD and its stages
   • Position of data mining in KDD process
   • Data selection, preprocessing, transformation flow
   • Interpretation and evaluation after mining
8. 7 Marks
   bDiscuss about data mining task primitives with examples.
   How to answer

   • Define data mining task primitives
   • Task, data, background knowledge primitives
   • Interestingness and presentation primitives
   • Examples for each primitive
9. 7 Marks
   aExplain major requirements and challenges in data mining.
   How to answer

   • Need for scalable and diverse data handling
   • Requirements: performance, background knowledge, interestingness
   • Challenges: high dimensionality, noise, privacy
   • Dynamic and distributed data issues
10. 7 Marks
    bExplain the data mining functionalities.
    How to answer

    • Define data mining functionalities
    • Characterization, discrimination, association
    • Classification, clustering, outlier analysis
    • Evolution and trend analysis
11. 7 Marks
    aExplain whether association rule mining is supervised or unsupervised type of learning.
    How to answer

    • Define supervised vs unsupervised learning
    • Nature of association rule mining without labels
    • Support, confidence framework example
    • Conclusion: unsupervised/descriptive type
12. 7 Marks
    bExplain briefly about statistical distribution-based outlier detection.
    How to answer

    • Define outlier and statistical approach
    • Block, discordancy tests and distributions
    • Parametric vs non-parametric models
    • Limitations for high-dimensional data
13. 7 Marks
    aWhat is data cleaning? What are the different techniques for handling missing values?
    How to answer

    • Define data cleaning and its need
    • Missing value causes and importance
    • Techniques: ignore, fill manually, mean/median, most probable
    • Comparison of techniques
14. 7 Marks
    bBriefly explain the differences between "classification" and "Clustering" and give an informal example of an application that would benefit from each technique.
    How to answer

    • Define classification vs clustering
    • Supervised vs unsupervised differences in table
    • Example application for classification
    • Example application for clustering
15. 7 Marks
    aExplain the following clustering methods in detail.

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

    • Define hierarchical clustering need
    • BIRCH: CF-tree and phases
    • CURE: representative points and shrinking
    • Advantages and applications
16. 7 Marks
    bExplain different data types used in clustering.
    How to answer

    • Need for different attribute types in clustering
    • Interval-scaled, binary, categorical variables
    • Ordinal, ratio-scaled and mixed types
    • Dissimilarity measures for each type