Tilka Manjhi Bhagalpur University 2007 M.C.A CS - 34 DATAWAREHOUSING AND MINING - Question Paper

COPYRIGHT RESERVED CS - 34

2007

Time : three hours

Full Marks : 80

Candidate are needed to provide their answers in
Their own words as far as possible.

The ques. are of equal value .
ans any 5 ques. in which
Q. No. one is compulsory.

1. Choose the accurate ans :
A. Which among is not available in web mining ?
a. Content mining
b. General access tracking trend
c. Search outcome mining
d. Pure content mining
B. Total column in record list in decision tree is :
a. 2
b. 3
c. 4
d. 1
C. Attribute list in decision table sorted by :
a. Attribute subject
b. Attribute value
c. Attribute node
d. Attribute topic
D. Diversity (before split) – diversity (left child ) + diversity (right child) is the formula of
a. Maximized diversity
b. Minimized diversity
c. Diversity measure
d. None
E. DBSCAN Stand for :
a. Diversity based spatial clustering of application of noise
b. Diversity based separate clustering of application of noise
c. Diversity based spatial clustering of application
d. None
F. In partition algorithm , construction involves for determining :
a. Minimal partition
b. Optimal partition
c. Maximum partition
d. None
G. Border algorithm maintains for frequent item sets :
a. Discovery
b. Support
c. Diversity
d. Confidence
H. Discovery of Association rule depends upon discovery of :
a. Item set
b. Frequent set
c. Partition set
d. None
I. Star schema is a paradigm for :
a. Modelling
b. Design
c. Code
d. None
J. KDD stand for :
a. Knowledge Database
b. Knowledge Discovery Database
c. Knowledge Division Database
d. None
2. Define the subsequent terms :
a. Data mart
b. OLAP system
c. ROLAP system
d. Meta Data
3. What do you mean about Data mining ? elaborate important tools of data mining ? Write the difference ranging from data mining and database .
4. (a) describe partition algorithm .
(b) Write stepwise example for priori algorithm for the subsequent item set :
{1} {2} {3} {4} {5} {6} {7} {8} {9}
two six six four eight five seven four 2
5. (a) What is K- Medoid ? Write its uses .
(b) describe DBScan Algorithm
6. Write the decision tree construction principle ? describe splitting indexes .
What is the use of decision tree in data mining .
seven (a) compute entropy and gini index for subsequent data :
T C1 C2 C3
100 40 30 30
(b) describe the role of attribute list and Record list in decision tree .
eight Write differences an any 2 of the subsequent :
(a) Structure and Unstructured text mining .
(b) Web page content mining and search request mining .
(c) KDD and Data mining .
(d) OLAP ROLAP
nine (a) What is association rule ? describe their uses .
(b) describe the subsequent :
a. Frequent set
b. Border set
c. Item sets

Earning:   Approval pending.