MGM’s
College of Engineering, Nanded.
Department of IT
Semester I (2014-15)
Class: BE(IT) Subject: DMDW Assignment III
___________________________________________________________________
Department of IT
Semester I (2014-15)
Class: BE(IT) Subject: DMDW Assignment III
___________________________________________________________________
1. Discuss
the essential steps of apriori algorithm.
2. Define:
1) Support 2) Confidence. From the following database, find out the items with Support ≥ 40 %.
Tid
|
Items
|
T1
|
M, O, N, K
|
T2
|
D,O,N
|
T3
|
M, A, K, E
|
T4
|
M, U, C, Y
|
T5
|
C,O,O,K
|
3. For
the above transaction database, find the itemsets having confidence ≥ 60 %.
4. Explain
the requirements for data mining GUI.
5. List
the ways to improve the efficiency of apriori algorithm.
6. Give
the comparison between Classification and Clustering.
7. What
are Iceberg queries? Explain with an example.
8. What
are the various forms of presenting and visualizing the discovered patterns?
9. What is
information gain? How Information Gain is calculated?
10. Discuss the Multilevel Association Rules mining
for transaction database.
11. State and explain the Bayesian algorithm.
12. Explain the decision tree induction algorithm.
13. What are the different techniques to calculate the
distance between patterns?
14. Explain the different types of cluster analysis
methods and discuss their features.
15.
Describe k-means algorithm and discuss its strengths and weaknesses.
16. The following table shows a set of paired data where x is the number of
years of work experience of a college graduate and y is corresponding salary of
the graduate. There is a linear relationship between the two variables, x and
y. Use Straight-line regression method with least squares and predict the
salary of a college graduate with 10 years of experience.
x years
experience
|
y salary (in
$1000s)
|
5
|
32
|
10
|
59
|
11
|
66
|
15
|
74
|
5
|
38
|
8
|
45
|
13
|
61
|
23
|
92
|
3
|
22
|
18
|
85
|
Faculty Incharge: Hashmi S A
