UNIT – I
1 Discuss about Data mining functionalities
2 Discuss about motivation for DM and why is it important
3 Briefly discuss the DW-applications
4 Explain data mining as a step in the process of knowledge discovery
5 Explain major issues in data mining
6 Explain Various steps in data pre-processing
7 Briefly discuss about data integration & data transformation
8 Discuss the role of data compression & numerosity reduction in data reduction Process
UNIT – II
1 Explain 3-tier DW – architecture
2 Explain about various OLAP operations
3 Differentiate between multi-dimensional OLAP & multi-operational OLAP
4 Draw & Explain the architecture for on-line analytical mining
5 Differentiate operational database systems and data warehouse
UNIT – III
1 What are the functional components of a data mining GUI
2 Discuss various Data mining primitives
3 Discuss about knowledge type to be mined
4 Give an example for DMQL queries by taking all data mining primitives and explain in detail
5 Write the syntax for the following data mining primitives
a) Task Relevant Data b) Concept Hierarchies
6 Write the syntax for the following data mining primitives
a) kind of knowledge to be mined b) measures in pattern interestingness
7 Describe why it is important to have a data mining query language
8 Explain the following 4 concept hierarchy types
a) schema b) set-grouping c) operation derived d) rule based
UNIT – IV
1 Explain the significance of analytical characterization. How is it performed.?
2 Discuss about data generalization and summarization based on characterization in detail
3 Write a short notes for the following in detail
a) Measuring the central tendency b) Measuring the dispersion of data
4 Explain the algorithm for attribute oriented induction. Explain the steps involved in it.
5 What are differences between concept description in large data bases and OLAP
6 Explain about the graph displays of basic statistical class description
UNIT – V
1 Explain Apriori algorithm with an example
2 Discuss about constraint based mining. Illustrate with an example
3 Write about association rule generation process
4 Write the FP-growth algorithm. Explain
5 How can we mine multi-level association rules efficiently using concept hierarchies.
6 Can we design a method that mines the complete set of frequent item sets without candidate generation. If yes, explain with an example
UNIT – VI
1 How prediction is different from classification
2 Compare and contrast classification methods
3 Discuss about K-nearest neighbor classifiers and case –based reasoning
4 Discuss about Back propagation classification
5 Explain about basic decision tree induction algorithm
6 Discuss Bayesian classification
7 Discuss about Prediction
UNIT – VII
1 Define clustering and describe the categorization of major clustering methods
2 Differentiate between agglomerative and divisive hierarchical clustering
3 Explain different types of data types used in cluster analysis
4 Discuss k-means algorithm
5 Discuss about density based methods
6 What is meant by Outlier analysis. Discuss about any one outlier detection
7 Explain about statistical based outlier detection and deviation based outlier detection
UNIT – VIII
1 Explain about different methods used for mining text databases
2 Discuss about time-series analysis
3 Discuss about Web Mining
4 What is sequential pattern mining? Explain
5 Discuss about Spatial data mining
1 Discuss about Data mining functionalities
2 Discuss about motivation for DM and why is it important
3 Briefly discuss the DW-applications
4 Explain data mining as a step in the process of knowledge discovery
5 Explain major issues in data mining
6 Explain Various steps in data pre-processing
7 Briefly discuss about data integration & data transformation
8 Discuss the role of data compression & numerosity reduction in data reduction Process
UNIT – II
1 Explain 3-tier DW – architecture
2 Explain about various OLAP operations
3 Differentiate between multi-dimensional OLAP & multi-operational OLAP
4 Draw & Explain the architecture for on-line analytical mining
5 Differentiate operational database systems and data warehouse
UNIT – III
1 What are the functional components of a data mining GUI
2 Discuss various Data mining primitives
3 Discuss about knowledge type to be mined
4 Give an example for DMQL queries by taking all data mining primitives and explain in detail
5 Write the syntax for the following data mining primitives
a) Task Relevant Data b) Concept Hierarchies
6 Write the syntax for the following data mining primitives
a) kind of knowledge to be mined b) measures in pattern interestingness
7 Describe why it is important to have a data mining query language
8 Explain the following 4 concept hierarchy types
a) schema b) set-grouping c) operation derived d) rule based
UNIT – IV
1 Explain the significance of analytical characterization. How is it performed.?
2 Discuss about data generalization and summarization based on characterization in detail
3 Write a short notes for the following in detail
a) Measuring the central tendency b) Measuring the dispersion of data
4 Explain the algorithm for attribute oriented induction. Explain the steps involved in it.
5 What are differences between concept description in large data bases and OLAP
6 Explain about the graph displays of basic statistical class description
UNIT – V
1 Explain Apriori algorithm with an example
2 Discuss about constraint based mining. Illustrate with an example
3 Write about association rule generation process
4 Write the FP-growth algorithm. Explain
5 How can we mine multi-level association rules efficiently using concept hierarchies.
6 Can we design a method that mines the complete set of frequent item sets without candidate generation. If yes, explain with an example
UNIT – VI
1 How prediction is different from classification
2 Compare and contrast classification methods
3 Discuss about K-nearest neighbor classifiers and case –based reasoning
4 Discuss about Back propagation classification
5 Explain about basic decision tree induction algorithm
6 Discuss Bayesian classification
7 Discuss about Prediction
UNIT – VII
1 Define clustering and describe the categorization of major clustering methods
2 Differentiate between agglomerative and divisive hierarchical clustering
3 Explain different types of data types used in cluster analysis
4 Discuss k-means algorithm
5 Discuss about density based methods
6 What is meant by Outlier analysis. Discuss about any one outlier detection
7 Explain about statistical based outlier detection and deviation based outlier detection
UNIT – VIII
1 Explain about different methods used for mining text databases
2 Discuss about time-series analysis
3 Discuss about Web Mining
4 What is sequential pattern mining? Explain
5 Discuss about Spatial data mining