VARDHAMAN COLLEGE OF ENGINEERING
(AUTONOMOUS)
B. Tech VI Semester Regular Examinations, May 2018
(Regulations: VCE-R15)
DATAWAREHOUSING AND DATA MINING
(Common to Computer Science and Engineering Information Technology)
Date: 17 May, 2018 FN
Time: 3 hours
Max Marks: 75
Answer ONE question from each Unit
All Questions Carry Equal Marks
Unit I
1.

Define attribute and discuss the types of an attribute which is determined by the set of possible values by proving suitable examples.
7M

Discuss issues to consider during data integration.
8M
2.

Discuss the Data Transformation Strategies used to transform or consolidate data into forms appropriate for mining.
7M

What is Data Mining? Describe the steps involved in data mining when viewed as a process of knowledge discovery.
8M
Unit II
3.

Briefly compare the following concepts. You may use an example to explain your point(s):
i. Data cleaning, data transformation, refresh
ii. Enterprise warehouse, data mart, virtual warehouse
8M

Write the star schema for IPL 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 spectators of Black Dog Pavilion at Chinnaswamy Stadium in 2018?
7M
4.

What are the optimization techniques for the efficient computation of data cubes? Explain any one.
6M

Suppose that a base cuboid has three dimensions with the following number of cells: 1000000, 100, and 1000. Suppose that each dimension is evenly partitioned into 10 portions for chunking:
i. Assuming each dimension has only one level, draw the complete lattice of the cube
ii. If each cube cell stores one measure with 4 bytes, what is the total size of the computed cube if the cube is dense
iii. State the order for computing the chunks in the cube that requires the least amount of space, and compute the total amount of main memory space required for computing the 2-D planes
9M
Unit III
5.

A database of transactions in a book mart is as follows: Let min-sup
Trans_ID
Items
101
Book, Pen, Eraser
102
Pen, Pencil
103
Notebook, Book, Pen, Eraser
104
Book, Pen
105
Book, Notebook, Eraser
Using the abbreviations B for Book, P for Pen, E for Eraser, PN for Pencil and N for Notebook, find all frequent itemsets using Apriori algorithm. Construct FP Tree, conditional pattern base and conditional FP Tree.
9M

A database has six transactions of purchase of books from a bookshop as given:
t1 CC, TC,CG} t2 D t3 CC,
t4 CC, D,CG t5 ANN, CC, t6 D
Let TC} and ANN,TC,CC}
Find the confidence and support of the association rule X Y and inverse rule X.
6M
Cont…2

6.

What is Market Basket Analysis? Explain by means of an example.
5M

A database has five transactions. Let min_sup 60 and min_conf=
TID
Items bought
T100

T200

T300

T400

T500

Find all frequent itemsets (individual alphabets) using Apriori and FP growth, respectively. Compare the efficiency of the two mining processes.
10M
Unit IV
7.

What is Classification and Prediction? Mention the criteria for comparing the Classification and prediction methods.
5M

List the factors which are contributing toward the usefulness of neural networks for classification and prediction in data mining. Discuss the working of back propagation.
10M
8.

Explain the steps involved in decision tree classification with a suitable example.
10M

Explain how lazy learners are useful in classification. Also list their limitations.
5M
Unit V
9.

How might you determine outliers in the data?
5M

Both k-means and k-medoids algorithms can perform effective clustering. Illustrate the strength and weakness of k-means in comparison with the k-medoids algorithm. Also, illustrate the strength and weakness of these schemes in comparison with a hierarchical clustering scheme.
10M
10.

Describe each of the following clustering algorithms in terms of the following criteria:
Shapes of clusters that can be determined
Input parameters that must be specified
Limitations
i. K-means
ii. K-medoids
iii. CLARA
6M

We have 8 points namely P3(10, P6(11, P8(12, forming 3 clusters. To start with you may take P1, P2, and P7 as initial centroids. Then apply K-Means clustering algorithm to calculate:
i. The centroid after one iteration
ii. The centroid after two iterations(the distance computation may be either Euclidean or Manhattan)