Anna University, Chennai
SRINIVASAN ENGINEERING COLLEGE,PERAMBALUR CS1011 DATA WAREHOUSING AND MINING
1. Explain the evolution of Database technology?
2.Explain the steps of knowledge discovery in databases?
3. Explain the architecture of data mining system?
4.Explain various tasks in data mining?(Or)
Explain the taxonomy of data mining tasks?
5.Explain various techniques in data mining?
1. Define Association Rule Mining.
1.Explain the issues regarding classification and prediction?
2.Write short notes on patterns?
3.Explain mining single –dimensional Boolean associated rules from transactional databases?
4.Explain apriori algorithm?
5.Explain how the efficiency of apriori is improved?
6.Explain frequent item set without candidate without candidate generation?
7.Explain mining Multi-dimensional Boolean association rules from transaction databases?
8.Explain constraint-based association mining?
1.Explain regression in predictive modeling?
2.Explain statistical perspective in data mining?
3.Explain Bayesian classification.
4.Discuss the requirements of clustering in data mining.
5.Explain the partitioning method of clustering.
6.Explain Visualization in data mining.
1.Define data warehouse?
1. Discuss the components of data warehouse.
2. List out the differences between OLTP and OLAP.
3.Discuss the various schematic representations in multidimensional model.
4. Explain the OLAP operations I multidimensional model.
5. Explain the design and construction of a data warehouse.
6.Expalin the three-tier data warehouse architecture.
7. Explain indexing.
8.Write notes on metadata repository.
9. Write short notes on VLDB.
1.Explain data mining applications for Biomedical and DNA data analysis.
2. Explain data mining applications fro financial data analysis.
3. Explain data mining applications for retail industry.
4. Explain data mining applications for Telecommunication industry.
5. Explain DBMiner tool in data mining.
6. Explain how data mining is used in health care analysis.
7. Explain how data mining is used in banking industry.
8. Explain the types of data mining.