(AUTONOMOUS)
B. Tech VII Semester Supplementary Examinations, May 2018
(Regulations: VCE-R14)
DATA MINING AND DATA WAREHOUSING
(Computer Science and Engineering)
Date: 21 May, 2018 AN Time: 3 hours Max Marks: 75
Answer ONE question from each Unit
All Questions Carry Equal Marks
Unit I
1. What are the major challenges of mining a huge amount of data (such as billions of
tuples) in comparison with mining a small amount of data (such as a few hundred tuple
data set).
8M
Illustrate the role of data mining as a confluence of multiple disciplines.
7M
2. Describe why concept hierarchies are useful in data mining. 7M
Give the differences between classification and prediction.
8M
Unit II
3. Suppose that we need to record three measures in a data cube: min, average, and
median. Design an efficient computation and storage method for each measure given
that the cube allows data to be deleted incrementally (that is in small portions at a time)
from the cube.
10M
Often, the aggregate measure value of many cells in a large data cuboid is zero, resulting
in a huge, yet sparse, multidimensional matrix. Design an implementation method that
can elegantly overcome this sparse matrix problem. Note that you need to explain your
data structures in detail and discuss the space needed, as well as how to retrieve data
from your structures.
5M
4. Explain in brief about data generalization. 5M
Draw the star schema for T20 cricket taking into account the spectator, location, game,
date for the centralized sales table. Starting with the base cuboid [date, spectator,
location, game] what specific OLAP operations should one perform in order to get the
total charges paid by Anna Pavilion spectators at Chidambaram Stadium in 2012?
10M
Unit III
5. Describe Frequent Pattern Mining with Vertical Data Format by means of an example. 5M
Some transactions in Big Bazaar are as follows:
TID List of items
T100 Colour TV, DVD, Speakers
T200 DVD, Laptop
T300 DVD, Desktop
T400 Colour TV, DVD, Laptop
T500 Colour TV, DVD, Desktop
The minimum support 30%. Using C for Colour TV, D for DVD, DT for Desktop, L for
Laptop and S for Speakers draw the FP Tree and corresponding conditional pattern bases
along with conditional FP Tree.
10M
6. Suppose that frequent itemsets are saved for a large transaction database, DB. Explain
how to efficiently mine the (global) association rules under the same minimum support
threshold if a set of new transactions, denoted as is (incrementally) added in?
6M
Association rule mining often generates a large number of rules. Explain effective
methods that can be used to reduce the number of rules generated while still preserving
most of the interesting rules.
9M
Cont…2

Unit IV
7. Why is tree pruning useful in decision tree induction? What is a drawback of using a
separate set of tuples to evaluate pruning?
10M
Given a decision tree, you have the option of:
i. Converting the decision tree to rules and then pruning the resulting rules, or
ii. Pruning the decision tree and then converting the pruned tree to rules. What
advantage does have over
5M
8. Illustrate the pre-processing steps that may be applied to the data to help improve the
accuracy, efficiency, and scalability of the classification process.
9M
Write an algorithm for k-nearest neighbor classification given k and the number of
attributes describing each tuple.
6M
Unit V
9. Describe the Grid-based and Model-based Clustering Methods. 8M
Explain with an example about the deviation-based outlier detection.
7M
10. How partitioning methods are useful in clustering? Describe any one classical
partitioning method.
7M
Explain with an example about Distance-Based Outlier Detection. 8M