CY-604-C Data Warehousing and Mining - 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
   aDefine data cleaning. Discuss the data cleaning techniques in brief.
   How to answer

   • Define data cleaning as preprocessing to remove noise and inconsistencies
   • List needs: missing values, noisy data, outliers, inconsistencies
   • Brief techniques: binning, regression, clustering, imputation, smoothing
2. 7 Marks
   bWhat is data warehouse? Explain the characteristics of data warehouse.
   How to answer

   • Define data warehouse as subject-oriented, integrated, time-variant, non-volatile collection
   • Explain Inmon definition
   • Explain four characteristics with examples
   • Contrast with operational database
3. 7 Marks
   aExplain Data Warehouse implementation techniques in detail.
   How to answer

   • Define implementation phases: planning, design, ETL, deployment
   • Explain top-down vs bottom-up approaches
   • Explain ETL, data marts, metadata, OLAP server layer
   • Discuss efficiency, maintenance and scalability issues
4. 7 Marks
   bWhat is OLAP? Elaborate the concept of ROLAP with neat diagram.
   How to answer

   • Define OLAP and its multidimensional analysis purpose
   • Define ROLAP with relational storage and SQL
   • Draw ROLAP architecture diagram
   • Differentiate ROLAP vs MOLAP briefly
5. 7 Marks
   aExplain Pros and Cons of Data Mining in brief.
   How to answer

   • Define data mining purpose
   • List pros: pattern discovery, prediction, business intelligence
   • List cons: privacy, cost, complexity, misuse
   • Conclude with balanced view
6. 7 Marks
   bWrite about Data Transformation methods.
   How to answer

   • Define data transformation in preprocessing
   • Explain smoothing, aggregation, generalization
   • Explain normalization and attribute construction
   • Give examples of each method
7. 7 Marks
   aWhat is Association Rule Mining? Discuss about the Boolean Association Rule Mining.
   How to answer

   • Define association rule mining with support and confidence
   • Define Boolean association rules for categorical presence/absence
   • Explain Apriori-like mining for Boolean rules
   • Give market-basket example
8. 7 Marks
   bWrite about Galaxy Schema for Multidimensional databases in detail.
   How to answer

   • Define multidimensional schema and need for galaxy schema
   • Explain multiple fact tables sharing conformed dimensions
   • Draw galaxy schema diagram with example
   • Compare with star and snowflake schema
9. 7 Marks
   aDefine Clustering. Discuss about important clustering methods in data mining.
   How to answer

   • Define clustering as unsupervised grouping by similarity
   • List categories: partitioning, hierarchical, density, grid, model-based
   • Brief each method with example algorithm
   • Mention applications
10. 7 Marks
    bWhat is learning phase in classification? Discuss Decision Tree Induction algorithm in detail.
    How to answer

    • Define classification two-step process and learning phase
    • Explain decision tree induction top-down greedy approach
    • Explain attribute selection, splitting, pruning
    • Draw example decision tree
11. 7 Marks
    aList and explain different types of data on which data mining is applied.
    How to answer

    • List data types: relational, transactional, spatial, multimedia, text, web, streams
    • Explain characteristics of each type
    • Give example of mining applied to each
    • Conclude on handling complexity
12. 7 Marks
    bWrite about Snowflake data warehouse schema in detail.
    How to answer

    • Define snowflake schema as normalized star schema
    • Explain fact table with normalized dimension hierarchies
    • Draw snowflake diagram example
    • Discuss advantages and disadvantages
13. 7 Marks
    aExplain the techniques to improve the efficiency of Apriori algorithm.
    How to answer

    • Recall Apriori bottleneck of candidate generation and scans
    • Explain hashing, transaction reduction, partitioning
    • Explain sampling and dynamic itemset counting
    • Explain effect on efficiency
14. 7 Marks
    bDifferentiate between top-down and bottom-up Hierarchical algorithms.
    How to answer

    • Define hierarchical clustering approaches
    • Contrast top-down divisive vs bottom-up agglomerative
    • Compare on basis of process, complexity, examples
    • Tabulate differences
15. 14 Marks
    Write short notes on any Two.

    1. a)OLAP Server
    2. b)Web Usage Mining
    3. c)Text Mining
    4. d)Data Prediction
    How to answer

    • Select any two topics from the four options
    • Define and explain key concepts for each
    • Mention architecture / steps / applications
    • Give example for each