M.C.A. (Semester IV) (CBCS) Examination Oct/Nov-2017
Science
DATA MINING AND WAREHOUSE
Day Date: Monday, 20-11-2017 Max. Marks: 70
Time: 02.30 PM to 05.00 PM
Instructions: Questions NO.1 and 2 are Compulsory.
Attempt any three questions from Q.NO.3 to Q.NO.7
Figures to the right indicate full marks.
Q.1 Choose the correct alternative given in the bracket. 10
system is customer oriented.
OLAP OLEP
OLTP None of these
2 A contains a subset of corporate-wide data that is of value
to a specific group of users.
Virtual warehouse Data mart
Enterprise warehouse None of these
3 which typically gathers data from multiple, heterogeneous,
and external sources.
Data extraction Data cleaning
Load None of these
4 In schema, the data warehouse contains a large central
table (fact table), and a set of smaller attendant tables (dimension
table).
Fact constellation Snowflake
Star None of these
5 This operation performs aggregation on a data cube, either by
climbing up the concept hierarchy for a dimension or by dimension
reduction.
Slice Dice
Rotate Roll-up
6 Which of the following is not a data mining functionality?
Characterization and Discrimination
Classification and regression
Selection Data House
Knowledge Data Definition
7 The full form of KDD is
Knowledge Database
Knowledge Discovery Database
Knowledge Data House
Knowledge Data Definition
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SLR-SM-29
8 The allows the selection of the relevant information
necessary for the data warehouse.
Top-down view Data warehouse view
Data source view Business query view
9 The data is stored, retrieved updated in
OLAP OLTP
SMTP FTP
1 Fact tables are
Completely demoralized Partially demoralized
Completely normalized Partially normalized
State True/False 04
A data-mining task can be specified in the form of a data-mining
query, which is input to the data mining system.
For the construction of decision tree classifiers require the domain
knowledge.
Data Integration is not the process of combining multiple data
source.
Data cleaning is process of adding noise and inconsistent data.
Q.2 Write a short note on following: 08
Drill-up operation
Data cleaning
Answer the following: 06
Explain agglomerative hierarchical clustering method with example.
What are the new trends in data mining? Explain it.
Q3 Answer the following: 14
What is data cube? Explain different forms of multidimensional data
model.
Explain OLAP operations with suitable example.
Q4 Answer the following: 14
Explain the multilevel associations rules from transactional data model.
State and explain data mining primitives with suitable example.
Q.5 Answer the following: 14
Explain the procedure of Bayesian classification method in detail.
State and explain the steps in k-means algorithm.
Q.6 Answer the following: 14
What is classification? Explain the issues regarding with classifications.
Explain various data mining applications.
Q.7 Answer the following: 14
What is Cluster analysis? Explain Model based clustering with example.
Explain additional themes on data mining.