AD-603-A Data Mining and Warehousing - May 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
   aWhat is the importance of data marts in data warehouse?
   How to answer

   • Define data mart and its types
   • Role in data warehouse architecture
   • Benefits: performance, access, departmental focus
   • Importance for OLAP and decision support
2. 7 Marks
   bExplain in detail about data preprocessing.
   How to answer

   • Define data preprocessing
   • Steps: cleaning, integration, transformation, reduction
   • Techniques for each step
   • Need with example
3. 7 Marks
   aCompare and contrast operational database systems with data warehouse.
   How to answer

   • Define OLTP vs data warehouse
   • Comparison on orientation, data, time span, users
   • Tabular differences
   • Example use cases
4. 7 Marks
   bDiscuss the typical OLAP operations with an example.
   How to answer

   • Define OLAP and data cube
   • Operations: roll-up, drill-down, slice, dice, pivot
   • Example dataset for each operation
   • Use in multidimensional analysis
5. 7 Marks
   aWith necessary diagrams and examples of data cubes explain various OLAP operations.
   How to answer

   • Define data cube and dimensions
   • Explain OLAP operations with cube diagrams
   • Examples for roll-up, drill-down, slice, dice, pivot
   • Neat labelled diagrams
6. 7 Marks
   bDiscuss about Data Warehouse hardware and operational design.
   How to answer

   • Hardware considerations: servers, storage, parallelism
   • Operational design: ETL, refresh, partitioning
   • Scalability and performance issues
   • Example architecture
7. 7 Marks
   aDescribe the various phases in knowledge discovery process with a neat diagram.
   How to answer

   • Define KDD process
   • Phases: selection, preprocessing, transformation, mining, evaluation
   • Neat block diagram of flow
   • Brief role of each phase
8. 7 Marks
   bDiscuss about fuzzy sets and fuzzy logic.
   How to answer

   • Define fuzzy set vs crisp set
   • Membership function concept
   • Fuzzy logic operators and rules
   • Application example
9. 7 Marks
   aIs the data warehouse prerequisite for data mining? Does the data warehouse helps data mining? If so in what ways explain?
   How to answer

   • Whether warehouse is prerequisite or not
   • How warehouse supports mining: clean, integrated data
   • Ways: OLAM, multidimensional mining
   • Conclusion with examples
10. 7 Marks
    bWhat is meant by classification? Justify why clustering is said to be supervised learning. How the classifier accuracy determined and also explains its various types.
    How to answer

    • Define classification vs clustering
    • Supervised vs unsupervised learning clarification
    • Classifier accuracy measures: holdout, cross-validation, confusion matrix
    • Types of classifiers
11. 7 Marks
    aExplain Rule based algorithm for classification. Discuss the usage of information gain in this.
    How to answer

    • Rule-based classification concept
    • Rule induction and pruning steps
    • Information gain formula and use in attribute selection
    • Example rule set
12. 7 Marks
    bDiscuss about K-nearest-neighbors algorithm.
    How to answer

    • Define KNN principle
    • Steps: distance measure, k selection, voting
    • Example with distance calculation
    • Advantages and limitations
13. 7 Marks
    aExplain different data types used in clustering.
    How to answer

    • Data types: interval, binary, categorical, ordinal, ratio
    • Dissimilarity measures for each type
    • Examples
    • Relevance to clustering
14. 7 Marks
    bDescribe example of data set for which Apriori check would actually increase the cost.
    How to answer

    • Brief Apriori principle and pruning
    • Condition where candidate generation overhead dominates
    • Example dataset with dense/long patterns
    • Why cost increases
15. 14 Marks
    Write short notes on any two :

    1. a)Data Warehouse design
    2. b)OLAP queries
    3. c)Data types and quality of data
    4. d)BIRCH algorithm
    How to answer

    • Select any two topics from list
    • Definition and key concepts for each
    • Diagram or steps where applicable
    • Applications and advantages