Download Example Questions for Final Exam All questions in all Course

Example Questions for Final Exam
All questions in all Course Studies (Course Study 1 – Course Study 7)
All questions in “Example Questions for Midterm Exam” document.
Part 1: Data Preparation before Data Mining
Part 2: Association Rule Mining
Part 3: Sequential Pattern Mining
Part 4: Classification Techniques in Data Mining (Bayes, Decision Tree, K-Nearest Neighbor)
Part 4: Classification Techniques in Data Mining (Neural Network)
1- Write a program code in any programming language to calculate the output of the neuron for classification.
I j   wij Oi   j
Oj 
i
1
1 e
I j
w1
x1
x2
y
w2
bias
For example: Input vector X=<1, 0, 1> weight1 = 0.2 weight2 = 0.4
weight3 = -0.5
wƟ=0.6 Ɵ = -0.4
0.6
-0.4
2- Train a perceptron only one step using the following activation function and parameter values.
T=Desired output
O=Actual output
T=0, O=1, W1=0.5, W2=0.3, Input1=2, Input2=1, W =-1, Ɵ = 1
Activation Function
 


1 : 
w i I i     0
O 


i

 

0
:
otherwise



w i (t  1)  w i (t )  w i (t )
w i (t )  (T  O)I i
3- Train a perceptron (until no error) to recognize logical AND using Step Function with threshold 0.2 and learning rate 0.1.
error e = Yexpected – Yactual
α is the learning rate
xi is an input
wi = wi + Δwi
Δwi = α * xi * e
4- In order to train neural network for the following dataset, how many input(s), weight(s) and output(s) are needed?
Department
Sales
Systems
Systems
Marketing
Age
30's
20's
40+
40+
Status
Junior
Junior
Senior
Senior
Sales Count
5
30
56
95
5- Solve the XOR problem using Unit Step activation function and following initial parameters.
Part 5: Clustering
6- Given three binary bit strings 010111, 101001, 111100, compute the dissimilarity matrix (distance matrix) for these three points
using the Jaccard distance metric.
7- Car-A and Car-B can be grouped together in the same cluster or not? Why?
Aspiration
(Std ?)
Yes
Yes
Car A
Car B
Fuel-type
(Diesel ?)
No
Yes
Num-of-doors
(4 ?)
No
No
Engine-location
(front ?)
No
Yes
Drive-wheels
(fwd ?)
Yes
No
Body-style
(sedan ?)
Yes
No
aspiration: std, turbo.
fuel-type: diesel, gas.
num-of-doors: four, two.
engine-location: front, rear.
drive-wheels: fwd, rwd.
body-style: sedan, hatchback
8- Given the 5 pts below show all steps for a 2-means clustering algorithm (k=2) where the initial mean estimates are (1.67, 0.67) &
(3.25, 1)
<2.1, 3.4>, <1.8, 3.4>, <0.7, 2.0>, <3.4, 2.0>, <3.6, 2.0>
Run the algorithm until the clusters do not change.
9- For the following data, find the cluster of sample <1,1>. (Use Euclidean distance)
Sample
1
2
3
X
1
2
1
Y
3
0
1
Cluster
1
2
?
10- Circle the clusters and draw dentograms for the following data points. Merge clusters based on three criteria: single link, complete
link, and average link.
11-Using k-means with k=2, calculate the first clusters for the data below. Table gives distances between pairs of points. Circle the
two clusters. Assume the initial cluster points are: (31,32) and (34,24).
11,6
11,6
0
11,38
12.6
15,18
35.2
20,40
22.8
25,24
15.1
26,8
32.8
31,32
32.8
34,24
29.2
40,41
45.4
43.47
52.0
11,38
15,18
20.40
24,24
26,8
31,32
34,24
40,41
43,47
32.0
12.6
35.2
22.8
15.1
32.8
29.2
45.4
52.0
0
20.3
9.2
19.8
33.5
20.9
26.9
29.1
33.2
20.4
0
22.6
11.7
14.9
21.2
20.0
34.0
40.3
9.2
22.5
0
16.8
32.6
13.6
21.3
20.0
24.0
19.8
11.7
16.8
0
16.3
10
9
22.7
29.2
33.5
14.9
32.6
16.0
0
24.5
17.9
35.8
42.5
20.9
21.2
13.6
10.0
24.5
0
8.5
12.7
19.2
26.9
19.9
21.3
9.0
17.9
8.5
0
18.0
24.7
29.2
34.0
20.0
22.7
35.8
12.7
18.0
0
6.7
33.2
40.3
24.0
29.2
42.5
19.2
24.7
6.7
0
Part 6: Outlier Detection
12- Network intrusion detection by using outlier detection methods is one of the well known data mining applications. Give an
example dataset which have the following columns:
Start Time
Src IP
Src Port
Dst IP
Dst Port
P
F
Service
Bytes
Attack or Normal
00:00:11.380
164.107.1.2
1026
205.188.2 54.195
4000
17
0
domain
56
ATTACK
00:00:11.384
216.65.138.227
1055
164.10 7.1.3
28001
17
0
snmptrap
36
NORMAL
00:00:11.384
164.107.1.3
28001
216.65.1 38.227
1055
17
0
N/A
68
NORMAL
....
....
....
.....
....
...
...
..
...
..
Then find the association rule(s) for detecting attacks with a minimum support value such that
Src IP=2006.163.27.95, Dest Port=139, Bytes Є [150, 200]  ATTACK (support %55)
Show the extraction of the association rule(s) step by step.
Part 7: Web Mining
13- Web Usage Mining - Write an example dataset and then extract some association rules related with web usage patterns.
For example:
o 60% of users who placed an online order in /company/product2, were in the 20-25 age group and lived on the West Coast
14 - We have a Web Log File in ECLF Format. Write a program code in any programming language for the following web mining
operation:
“Which web page was mostly called from which web page”.
For Example:
IP Address
Time/Date
Method/URI
Referrer
Agent
202.120.224.4
15:30:01/2-Jan-01
GET Index.htm
http://ok.edu/link.htm
Mozilla/4.0(IE5.0W98)
202.120.224.4
15:30:01/2-Jan-01
GET 1.htm
http://ex.edu/index.htm
Mozilla/4.0(IE5.0W98)
202.120.224.4
15:30:01/2-Jan-01
GET A.htm
http://ex.edu/index.htm
Mozilla/4.0(IE5.0W98)
202.120.224.4
15:33:04/2-Jan-01
GET Index.htm
http://ok.edu/res.php
Mozilla/4.0(IE4.0NT)
202.120.224.4
15:33:04/2-Jan-01
GET 1.htm
http://ex.edu/index.htm
Mozilla/4.0(IE4.0NT)
202.120.224.4
15:33:04/2-Jan-01
GET A.htm
http://ox.edu/index.htm
Mozilla/4.0(IE4.0NT)
202.120.224.4
15:35:11/2-Jan-01
GET 1.htm
http://ok.edu/A.htm
Mozilla/4.0(IE5.0W98)
202.120.224.4
15:35:11/2-Jan-01
GET B.htm
http://ex.edu/A.htm
Mozilla/4.0(IE4.0NT)
202.120.224.4
15:37:09/2-Jan-01
GET A.htm
http://ox.edu/C.htm
Mozilla/4.0(IE5.0W98)
15- Write an example for each web mining categories.
Part 8: Text Mining
16- Write the output text after we apply feature generation step of Text Mining Process.
A computer is a machine that manipulates data according to a list of instructions. The first devices that resemble modern computers
date to the mid-20th century (around 1940 - 1945), although the computer concept and various machines similar to computers existed
earlier. Early electronic computers were the size of a large room, consuming as much power as several hundred unmodern personal
computers.
17- Text Mining: Use association rule mining techniques to find most related words in the following five text documents.
For example: “98% of the documents which are interested on apple do it related with the chromatography”.
X  Y: “apple  chromatography”
Doc1
Doc2
Doc3
Doc4
Doc5
Sea refers to water of the ocean.
Large lakes are sometimes referred to as inland seas.
A sea is a large expanse of water.
Why is sea water salty, and not lake water?
Lake is not large as sea water.