Important questions for DM&DW

Valid for: BCA & Others/AU and all Andhra Pradesh universities

SUB: Data Mining and Data Warehousing (DM & DW)

## UNIT 1: INTRODUCTION TO DATA MINING

1. Define data mining and its importance.

2. Describe about KDD process with a diagram.

3. Explain about Architecture of data mining.

4. Define these terms in brief A. Data mining functionalities

B. Central tendency with examples

5. About the Measure of dispersion of data?

6. Describe data preprocessing in brief.

7. Describe data cleaning and integration in brief. (Optional)

8. Define data discretization with an example. (Optional)

9. Define these in brief A. Data Warehouses

B. Relational databases

10. How data mining is motivated and what kind of data is mined?

## UNIT 2: DATA WAREHOUSE AND OLAP TECHNOLOGY

1. Define the data warehouse and explain about multidimensional data model.

2. Describe about data cube, star schema, snowflake schema, and fact constellation schema with diagrammatic examples.

3. Differences between OLAP and OLTP.

4. Describe data warehouse architecture with a diagrammatic presentation.

5. Describe about OLAP, ROLAP, MOLAP, OLTP, and OLAP query processing. (Optional)

6. Define concept hierarchy with any two types of hierarchies. (Optional)

7. Explain star schema and fact constellation schema.

8. Differences between star schema and snowflake schema?

9. Describe about metadata repository, data warehouse models, and data cube.

10. Briefly describe the three-tier data warehouse architecture with a diagrammatic presentation.

## UNIT 3: MINING FREQUENT PATTERNS, ASSOCIATIONS & CORRELATIONS

1. Describe the apriori algorithm in brief with examples and its applications.

2. Define the FP-growth algorithm with examples and its advantages.

3. What are the major advantages and disadvantages of apriori algorithm?

4. Give your own example to perform an apriori algorithm with a flowchart.

5. Steps to construct an FP-growth algorithm with an example?

## UNIT 4: CLASSIFICATION AND PREDICTION

1. Describe classification and prediction with types.

2. Major issues by classification and prediction? (Optional)

3. Classify decision tree induction with an example.

4. Describe Bayesian classification with an example.

5. Describe Naive Bayesian classification and Bayesian belief networks in detail.

6. Define IF-THEN rules for classification. (Optional)

7. Explain various types of Naive Bayes classifiers.

8. About classification analysis, Hunt's algorithm, and example for decision tree induction?

9. Define tree pruning and rule-based classifier. (Optional)

10. What are the major methods involved in expressing attribute test conditions? (Optional)

## UNIT 5: CLUSTER ANALYSIS

1. Define cluster analysis and its types.

2. Describe major clustering methods with examples.

3. Describe various partitioning methods.

4. Explain about K-means algorithm in detail.

5. Describe the BIRCH algorithm, density-based methods, and Outlier analysis with examples.