M.C.A (Semester IV) (CBCS) Examination Nov/Dec-2018
Science
DATA MINING AND WAREHOUSE
Time: 2½ Hours Max. Marks: 70
Instructions: Q.1 and Q.2 are compulsory.
Attempt any three questions from Q. 3 to 7.
Figures to the right indicate full marks.
Q.1 Choose correct alternative: 10
is a visualization operation that rotates the data axes in
view to provide an alternative data presentation.
Slice Roll-up
Pivot Drill-down
which typically gathers data from multiple, heterogeneous,
and external sources.
Load Data extraction
Refresh Data cleaning
An system is customer-oriented and is used for transaction
and query processing by clerks, clients, and information technology
professionals.
OLTP OLAP
OLEP None of these
An collects all of the information about subject spanning
the entire organization.
Virtual warehouse True warehouse
Data Mart Enterprise warehouse
schema is also known as galaxy schema.
Star Snowflake
Fact constellation Hybrid
selectively compute a proper subset of the whole set of
possible cuboids.
Medium materialization Partial materialization
No materialization Full materialization
hierarchical clustering methods uses a bottom-up strategy.
Agglomerative Divisive
Alternative None of these
is the probability, of H conditioned on X.
Prior Posterior
Postfix Maximum
The algorithm is sensitive to outliers because such objects
are far away from the majority of the data.
K-mediods K-maximum
K-means K-minimum
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SLR-SN-20
10) The class label of each training tuple is provided in
supervised learning unsupervised learning
blind learning none of these
State True or False.
Dimensionally reduction techniques replace the original data volume by
alternative, smaller forms of data representation.
Drill-down operation performs aggregation on a data cube, either by
climbing up a concept hierarchy for a dimension or by dimension
reduction.
Virtual warehouse is a set of views over operational databases.
Star schema is also known as galaxy schema.
04
Q.2 Write Short notes on.
Market Basket Analysis
Outlier Analysis
08
Answer the following.
What is data cube? Explain the use of snowflake schema in short.
What is metadata? Explain it.
06
Q.3 Answer the following.
Explain various data mining primitives with example. 07
What is Data warehouse? Explain the different between OLTP and OLAP. 07
Q.4 Answer the following.
Explain Apriori Algorithm with example. 07
Explain the procedure for decision tree induction method. 07
Q.5 Answer the following.
Explain various data mining Applications. 07
How naive Bayesian classifier works? Explain with suitable example. 07
Q.6 Answer the following.
What is cluster Analysis? Explain various requirements for cluster analysis. 07
Explain the steps in K-medoids algorithm with suitable example. 07
Q.7 Answer the following.
Explain the different types of hierarchical clustering methods. 07
Explain OLAP operations with suitable example. 07