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Rayalaseema University (UG)
Data Mining and Warehousing
III B.Com(CA) and III B.A(CA)
10
Marks Important Questions
Unit-I
- Explain about Data mining functionalities.
- Explain about Data pre-processing.
- Explain about data cleaning and data integration.
- Explain about data reduction, data transformation.
Unit-II
- Explain about Data Warehouse Multi tiered (Three Tiers) Architecture.
- Explain about Data Cube.
- Explain about OLAP.
- Explain about Data warehouse design and implementation.
Unit-III
- Explain about Market Basket Analysis a Motivating Example
- Explain
about Frequent
item set mining methods any two algorithms:
- Explain about APRIORI Algorithm with example.
- Explain about FP (Frequent
Patterns) -growth
Algorithm with example.
Unit-IV
- Explain about Decision tree induction,
- Explain in briefly Bayes classification,
- Explain about Any two advanced methods, model evaluation.
Unit-V
- What are the Major clustering approaches (or) Explain about cluster?
- Explain about Partitioning methods (or) Explain about k-Means Method and k-Medoids Method.
- Explain about Hierarchical methods.
- Explain about Grid-Based Methods (or) Explain about DBSCAN method with example.
4
Marks Important Questions
- What is data mining?
- Explain about Data and attributes types.
- Explain about statistical description of data.
- Explain about data discretization.
- Explain about Data models.
- Explain about Stars, Snowflakes, and Fact Constellations: Schemas for Multidimensional models.
Explain about pattern evaluation methods.
- Explain about Classification basic concepts.
- What is Cluster Analysis? (Or) Explain bout Cluster Analysis, its applications and its features.
| Public Exam Papers April 2019 |  |
| Public Exam Papers April 2020 | |
| Public Exam Papers April 2018 | |
| Study Notes | |
| Syllabus | |
Rayalaseema University (UG)
Data Mining and Warehousing
III B.Com(CA) and III B.A(CA)
10
Marks Important Questions
Unit-I
- Explain about Data mining functionalities.
- Explain about Data pre-processing.
- Explain about data cleaning and data integration.
- Explain about data reduction, data transformation.
Unit-II
- Explain about Data Warehouse Multi tiered (Three Tiers) Architecture.
- Explain about Data Cube.
- Explain about OLAP.
- Explain about Data warehouse design and implementation.
Unit-III
- Explain about Market Basket Analysis a Motivating Example
- Explain
about Frequent
item set mining methods any two algorithms:
- Explain about APRIORI Algorithm with example.
- Explain about FP (Frequent
Patterns) -growth
Algorithm with example.
Unit-IV
- Explain about Decision tree induction,
- Explain in briefly Bayes classification,
- Explain about Any two advanced methods, model evaluation.
Unit-V
- What are the Major clustering approaches (or) Explain about cluster?
- Explain about Partitioning methods (or) Explain about k-Means Method and k-Medoids Method.
- Explain about Hierarchical methods.
- Explain about Grid-Based Methods (or) Explain about DBSCAN method with example.
4
Marks Important Questions
- What is data mining?
- Explain about Data and attributes types.
- Explain about statistical description of data.
- Explain about data discretization.
- Explain about Data models.
- Explain about Stars, Snowflakes, and Fact Constellations: Schemas for Multidimensional models.
Explain about pattern evaluation methods.
- Explain about Classification basic concepts.
- What is Cluster Analysis? (Or) Explain bout Cluster Analysis, its applications and its features.
| Public Exam Papers April 2019 | |
| Public Exam Papers April 2020 | |
| Public Exam Papers April 2018 | |
| Study Notes | |
| Syllabus | |
