DMBI Important Questions For GTU Exam
1. Explain KDD process
- Visualization techniques in KDD.
2. Differentiate between OLTP and OLAP.
3. Explain schemas
- Star schema
- Snowflake schema
- Fact Constellations schema
4. Apriori algorithm Example
- State the Apriori Property
- Example - Generate frequent itemsets and generate association rules based (Given : Minimum support and minimum confidence).
- Methos to Improve Apriori's Efficiency
5. Min-Max normalization method Example
6. Differentiate Classification and Prediction
- Issues in classification and prediction.
- Why naïve Bayesian classification is called “naïve”? Describe naïve Bayesian classification with example.
7. Data Smoothing
- data smoothing by bin mean.
- by bin partition (equal frequency)
- by bin means
- by bin medians
- by bin boundaries
- Mean, Median, Mode,Variance, Standard Deviation.
8. Data Cleaning
- Methods for Handling missing values.
9. Data warehouse architecture.
10. OLAP Operations in multidimensional Model
- or Data Cube Operations in multidimensional Model
11. Major issues in Data Mining.
12. Decision Tree
13. Hadoop Architecture
- HDFS (Hadoop Distributed File System)
14. Text Mining, Web Mining , Spatial Mining
15. Enlist the preprocessing steps with example. Explain procedure of any technique of preprocessing.
16. Linear and nonlinear regression.
17. How data Mining is useful for Business Intelligence applications as follows :
- Balanced Scorecard
- Fraud Detection
- Clickstream Mining
- Market Segmentation
- Retail industry
- Telecommunications
- Industry
- Banking & finance
18. Data Mart and Types
- Data Mart , Enterprise Warehouse & Virtual Warehouse
19. Big Data. Discuss various applications of Big Data.
20. Explain mapreduce. Explain any example using mapreduce.
21. Definition
- Data Warehouse
- Business Intelligence
- Data Mart
- Regression
- Outlier Analysis
- Visualization techniques in KDD.
2. Differentiate between OLTP and OLAP.
3. Explain schemas
- Star schema
- Snowflake schema
- Fact Constellations schema
4. Apriori algorithm Example
- State the Apriori Property
- Example - Generate frequent itemsets and generate association rules based (Given : Minimum support and minimum confidence).
- Methos to Improve Apriori's Efficiency
5. Min-Max normalization method Example
6. Differentiate Classification and Prediction
- Issues in classification and prediction.
- Why naïve Bayesian classification is called “naïve”? Describe naïve Bayesian classification with example.
7. Data Smoothing
- data smoothing by bin mean.
- by bin partition (equal frequency)
- by bin means
- by bin medians
- by bin boundaries
- Mean, Median, Mode,Variance, Standard Deviation.
8. Data Cleaning
- Methods for Handling missing values.
9. Data warehouse architecture.
10. OLAP Operations in multidimensional Model
- or Data Cube Operations in multidimensional Model
11. Major issues in Data Mining.
12. Decision Tree
13. Hadoop Architecture
- HDFS (Hadoop Distributed File System)
14. Text Mining, Web Mining , Spatial Mining
15. Enlist the preprocessing steps with example. Explain procedure of any technique of preprocessing.
16. Linear and nonlinear regression.
17. How data Mining is useful for Business Intelligence applications as follows :
- Balanced Scorecard
- Fraud Detection
- Clickstream Mining
- Market Segmentation
- Retail industry
- Telecommunications
- Industry
- Banking & finance
18. Data Mart and Types
- Data Mart , Enterprise Warehouse & Virtual Warehouse
19. Big Data. Discuss various applications of Big Data.
20. Explain mapreduce. Explain any example using mapreduce.
21. Definition
- Data Warehouse
- Business Intelligence
- Data Mart
- Regression
- Outlier Analysis
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