DATA WAREHOUSE AND DATA MINING QUESTION
DATA WAREHOUSE AND DATA MINING QUESTION BANK
UNIT I
PART A
1. Define Data warehouse?
2. Write the components of Data warehouse?
3. What is Database schema?
4. Define Metadata?
5. Write the transformation tools used in Data warehouse?
6. What is Data Extraction?
7. Write any three characteristics of Data warehouse?
8. List out the benefits of Data warehouse?
9. Write the limitations of Data Warehouse?
10. What is Data marts?
11. Define OLAP?
12. List out the types of OLAP?
13. List out the views in the design of a data warehouse?
14. List out the steps of the data warehouse design process?
15. Define HOLAP?
16. What are dependent and independent data marts?
17. Define indexing?
18. Write the three tiers in Data warehouse Architecture?
19. List out the OLAP operations?
20. How a database design is represented in OLAP systems?
PART B
1.
2.
3.
4.
5.
Explain the components of Data warehouse in detail.
Explain the architecture of Data warehouse in detail with a neat diagram.
Explain OLAP and its types.Also explain the advantages of OLAP.
Write in detail about DBMS schema.
Write short notes on the following. (a) Data extraction (b) Clean up © Transformation
tools
6. Define Data warehouse? Write its components,characteristics,benefits and limitation?
UNIT II
PART-A
1.Define Data Mining?
2.Define KDD process?
3.Write the goals of Data Mining?
4.List out the applications of Data Mining?
5.Write down the tools used in Data Mining?
6.Define Concept hierarchy?
7.Give some alternative terms for data mining.
8. Define Association Rule Mining.
9. Define support and confidence in Association rule mining.
10.How are association rules mined from large databases?
11.List out the different classifications of Association rule mining.
12.Give few techniques to improve the efficiency of Apriori algorithm.
13.What are multidimensional association rules?
14.Mention few approaches to mining Multilevel Association Rules.
15.Define constraint-Based Association Mining.
16.Mention some of the data mining techniques.
17.Define descriptive model.
18.Define Predictive model.
19.What are the steps involved in KDD process.
20.What are the things suffering the performance of Apriori candidate generation
technique.
PART B
1.Define Data Mining?Explain the steps followed in Mining process?
2.Explain in detail about KDD process.
3.Write short notes on the following.(a)Data Preprocessing (b)Data Discretization (c)Concept
Hierarchy
4. Explain Apriori Algorithm.
5.Explain Association Rule Mining.
6.Explain FP Growth algorithm.
UNIT III
PART-A
1.Define the concept of classification.
2.What is Decision tree?
3.What is Attribute Selection Measure?
4.Describe Tree pruning methods.
5.Define Pre Pruning
6.Define Post Pruning.
7.Define the concept of prediction.
8.What is Bayesian Belief Networks?
9.What is Input layer?
10.What is Hidden Layer?
11.What is Output layer?
12.What are the steps followed in Backpropagation?
13.Define SVM?
14.What are the steps followed in Associative Classification?
15.What is Frequent pattern-Based Classification?
16.Define Lazy learners?
17.Define CBR?
18.List out the other Classification Methods?
19.What is Splitting Criterion?
20.List out the Attribute Selection Measures?
PART-B
1.Explain Decision Tree Induction.
2.Explain the Classification by Back propagation.
3.Explain the Classification by Decision Tree Induction.
4.Explain Lazy learners.
5.Explain any two Classification Methods.
6.Explain the Process of Back Propagation.
UNIT IV
PART A
1. What is cluster analysis?
2. What are the types of data in cluster analysis?
3. Define clustering.
4. What are the requirements for cluster analysis?
5. What is hierarchical method?
6. What is CURE?
7. Define density based method.
8. What is DBSCAN?
9. What is Grid based method?
10. What are the two basic approaches in model based method?
11. What are the two approaches used by regression to perform classification?
12. What is the use of Regression?
13. What are the fields in which clustering techniques are used?
14. Define CLARA
15. What is a STING?
16. Define Outlier analysis.
17. What are the types of outlier analysis.
18. Define collective outliers.
19. Define Global outliers.
20. Define conceptual outliers.
PART B
1.
2.
3.
4.
5.
What is an outlier? Explain the types of outliers with diagram in detail.
Explain k means partitioning method in detail.
What are the requirements for cluster analysis?Explain briefly.
Briefly explain outlier analysis in detail.
Explain the following: (a) cluster homogeneity (b)cluster completeness © Rag bag (d)
Small cluster preservation.
6. Write Short notes on (a)Global outliers (b)contextual outliers (c)Collective outliers
]
UNIT V
PART-A
1.Define text mining
2.What does web mining mean?
3.Define spatial data mining.
4.Define multimedia data mining.
5.What are the two approaches used in Multimedia mining?
6.List out the features in Web data mining?
7.What is Content-Based retrieval?
8.Write the application of content based retrieval?
9.What are the elements in Spatial Data mining
10.Write the two type of Queries in Multimedia data mining?
11.List out the three types of Queries in Spatial data mining?
12.What are the two approaches used in Spatial data mining?
13.How data mining is used in banking industry?
14.Write the types of data mining.
15.Define data query
16.Define knowledge Query.
17.What are the classifications tools for data mining?
18.Describe the use of DBMiner.
19.What are the elements in Multimedia Data Mining?
20.What is Spatial database?
PART-B
1.Explain Multimedia Data Mining.
2.Explain Spatial Data Mining.
3.What is Spatial Data mining?Explain the techniques used in Spatial Data mining.
4.Explain Multimedia Queries.
5.Explain Text Data mining.
6.Explain Web Mining.
UNIT I
PART A
1. Define Data warehouse?
2. Write the components of Data warehouse?
3. What is Database schema?
4. Define Metadata?
5. Write the transformation tools used in Data warehouse?
6. What is Data Extraction?
7. Write any three characteristics of Data warehouse?
8. List out the benefits of Data warehouse?
9. Write the limitations of Data Warehouse?
10. What is Data marts?
11. Define OLAP?
12. List out the types of OLAP?
13. List out the views in the design of a data warehouse?
14. List out the steps of the data warehouse design process?
15. Define HOLAP?
16. What are dependent and independent data marts?
17. Define indexing?
18. Write the three tiers in Data warehouse Architecture?
19. List out the OLAP operations?
20. How a database design is represented in OLAP systems?
PART B
1.
2.
3.
4.
5.
Explain the components of Data warehouse in detail.
Explain the architecture of Data warehouse in detail with a neat diagram.
Explain OLAP and its types.Also explain the advantages of OLAP.
Write in detail about DBMS schema.
Write short notes on the following. (a) Data extraction (b) Clean up © Transformation
tools
6. Define Data warehouse? Write its components,characteristics,benefits and limitation?
UNIT II
PART-A
1.Define Data Mining?
2.Define KDD process?
3.Write the goals of Data Mining?
4.List out the applications of Data Mining?
5.Write down the tools used in Data Mining?
6.Define Concept hierarchy?
7.Give some alternative terms for data mining.
8. Define Association Rule Mining.
9. Define support and confidence in Association rule mining.
10.How are association rules mined from large databases?
11.List out the different classifications of Association rule mining.
12.Give few techniques to improve the efficiency of Apriori algorithm.
13.What are multidimensional association rules?
14.Mention few approaches to mining Multilevel Association Rules.
15.Define constraint-Based Association Mining.
16.Mention some of the data mining techniques.
17.Define descriptive model.
18.Define Predictive model.
19.What are the steps involved in KDD process.
20.What are the things suffering the performance of Apriori candidate generation
technique.
PART B
1.Define Data Mining?Explain the steps followed in Mining process?
2.Explain in detail about KDD process.
3.Write short notes on the following.(a)Data Preprocessing (b)Data Discretization (c)Concept
Hierarchy
4. Explain Apriori Algorithm.
5.Explain Association Rule Mining.
6.Explain FP Growth algorithm.
UNIT III
PART-A
1.Define the concept of classification.
2.What is Decision tree?
3.What is Attribute Selection Measure?
4.Describe Tree pruning methods.
5.Define Pre Pruning
6.Define Post Pruning.
7.Define the concept of prediction.
8.What is Bayesian Belief Networks?
9.What is Input layer?
10.What is Hidden Layer?
11.What is Output layer?
12.What are the steps followed in Backpropagation?
13.Define SVM?
14.What are the steps followed in Associative Classification?
15.What is Frequent pattern-Based Classification?
16.Define Lazy learners?
17.Define CBR?
18.List out the other Classification Methods?
19.What is Splitting Criterion?
20.List out the Attribute Selection Measures?
PART-B
1.Explain Decision Tree Induction.
2.Explain the Classification by Back propagation.
3.Explain the Classification by Decision Tree Induction.
4.Explain Lazy learners.
5.Explain any two Classification Methods.
6.Explain the Process of Back Propagation.
UNIT IV
PART A
1. What is cluster analysis?
2. What are the types of data in cluster analysis?
3. Define clustering.
4. What are the requirements for cluster analysis?
5. What is hierarchical method?
6. What is CURE?
7. Define density based method.
8. What is DBSCAN?
9. What is Grid based method?
10. What are the two basic approaches in model based method?
11. What are the two approaches used by regression to perform classification?
12. What is the use of Regression?
13. What are the fields in which clustering techniques are used?
14. Define CLARA
15. What is a STING?
16. Define Outlier analysis.
17. What are the types of outlier analysis.
18. Define collective outliers.
19. Define Global outliers.
20. Define conceptual outliers.
PART B
1.
2.
3.
4.
5.
What is an outlier? Explain the types of outliers with diagram in detail.
Explain k means partitioning method in detail.
What are the requirements for cluster analysis?Explain briefly.
Briefly explain outlier analysis in detail.
Explain the following: (a) cluster homogeneity (b)cluster completeness © Rag bag (d)
Small cluster preservation.
6. Write Short notes on (a)Global outliers (b)contextual outliers (c)Collective outliers
]
UNIT V
PART-A
1.Define text mining
2.What does web mining mean?
3.Define spatial data mining.
4.Define multimedia data mining.
5.What are the two approaches used in Multimedia mining?
6.List out the features in Web data mining?
7.What is Content-Based retrieval?
8.Write the application of content based retrieval?
9.What are the elements in Spatial Data mining
10.Write the two type of Queries in Multimedia data mining?
11.List out the three types of Queries in Spatial data mining?
12.What are the two approaches used in Spatial data mining?
13.How data mining is used in banking industry?
14.Write the types of data mining.
15.Define data query
16.Define knowledge Query.
17.What are the classifications tools for data mining?
18.Describe the use of DBMiner.
19.What are the elements in Multimedia Data Mining?
20.What is Spatial database?
PART-B
1.Explain Multimedia Data Mining.
2.Explain Spatial Data Mining.
3.What is Spatial Data mining?Explain the techniques used in Spatial Data mining.
4.Explain Multimedia Queries.
5.Explain Text Data mining.
6.Explain Web Mining.