1.
Explain how
Classification is different from Prediction? Discuss various issues in
classification and prediction and write criteria to compare classification
an prediction methods.
2.
Explain
Knowledge Discovery in Database process. Why KDD process is
popular as
Data Mining Process.
3.
On what kind
of data, data mining can be applicable? Explain in brief.
4.
Explain the
Attribute Oriented Induction in brief.
5.
Explain
Concept Description and Data Generalization using specific example.
6.
Define Data
Warehouse. Discuss the architecture of Data Warehouse.
7.
Discuss
various OLAP operations.
8.
What is the
need of Online Analytical Processing (OLAP)? List categories of OLAP tools
and explain any one.
9.
Explain BI/DW
architecture in detail.
10.
Describe
classification methods : CART and rough set approach.
11.
Briefly outline
the major steps of decision tree classification
12. How
can we integrate a Data mining system with a Database or a Data
Warehouse.
13.
Describe
major issues in data mining.
14.
Explain
Discrimination and classification.
15.
Explain
Characterization and clustering.
16.
Explain
Classification and prediction.
17.
Explain
three-tier Data Warehousing architecture.
18.
Explain FP
Growth to generate frequent item sets using specific example
19.
Explain generalization
using attribute oriented induction with a complete example. Show necessary
steps.
20. Explain
Apriori alfo in detail. What are the limitations of Apriori algorithm and how can
we increase the efficiency of Apriori Algorithm.
21. Difference
between OLTP and OLAP systems.
22. Expalin
Star and Snowflake schema in detail.
23. Explain
following terms :
-
Support
-
Confidence
-
Association Rules
-
Frequent item sets.
24.
Define tree
pruning. Why is tree pruning useful in decision tree induction? How
classification rules extracted from decision trees.
25.
Discuss following:
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Thursday, May 23, 2013
BIDM ASSIGNMENT LIST
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