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QP CODE

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SEM-VI

Subject:- Pattern Analysis and Business Intelligence Time: 02.00 Hours

Max. Marks: 60

N.B 1. Q.1 is compulsory

2. Attempt any two from the remaining three questions

3. Each Question carry 20 marks.

Date: 17/ 06/2023

Q.1. Attempt All Marks

a) Explain issues in Data Mining.

Any 5 issues- 1 mark each

5

b) Discuss on types of attributes with example.

Types of attributes- At least 2 with example-2.5 marks each

5

c)

Explain DBSCAN method in detail.

Density based clustering- 2 Marks

DBSCAN algorithm- 3 Marks

5

d)

Define Decision support system with example.

Definition-1 Mark

Features- 2 Marks

Components & advantages-2 Marks

5

Q.2. Attempt All

a) Explain datamining steps in KDD.

Steps of KDD-4 Marks

4

b)

Find Mean, Median, Mode for the given data.

11, 13, 13, 15, 15, 16, 19, 20, 20, 21, 21, 22, 23, 24, 30, 40, 45, 45, 45

Solution:

Mean= 22.94

Median= 21

Mode= 45

4

c)

Draw BI architecture. Explain any one BI application where data mining

can be used.

BI architecture diagram-3 Marks

Explanation of BI application-3 Marks

6

d)

Discuss Supervised and Unsupervised outlier detection methods.

Supervised outlier detection method- 3 Marks

Unsupervised outlier detection method- 3 Marks

6

Q.3. Attempt All

PILLAI COLLEGE OF ENGINEERING, NEW PANVEL

(Autonomous) (Accredited ‘A+’ by NAAC)

END SEMESTER EXAMINATION

June 2023

BRANCH: Information Technology

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QP CODE

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a)

Discuss on bagging and boosting of classifiers?

Bagging -2 Marks

Boosting -2 Marks

4

b)

Partitions the given data into 4 bins using equal width binning method and

perform smoothing according to

(1) Smoothing by bin mean

(2) Smoothing by bin median

11, 13, 13, 15, 15, 16, 19, 20, 20, 20, 21, 21, 22, 23, 24, 30, 40, 45,

45, 45, 71, 72, 73, 75

Solution: (2 Marks each for smoothing technique)

No. of elements in 1 Bin: 24/4=6

(1) Smoothing by bin mean

Bin 1:[11, 13, 13, 15, 15, 16]= 13.83 (all elements will be replaced by

mean)

Bin 2: [19, 20, 20, 20, 21, 21]= 20.17

Bin 3: [22, 23, 24, 30, 40, 45]= 30.67

Bin 4: [45, 45, 71, 72, 73, 75]= 63.5

(2) Smoothing by Median

Bin 1:[11, 13, 13, 15, 15, 16]= 14 (all elements will be replaced by

median)

Bin 2: [19, 20, 20, 20, 21, 21]= 20

Bin 3: [22, 23, 24, 30, 40, 45]= 27

Bin 4: [45, 45, 71, 72, 73, 75]= 71.5

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c)

Consider the following dataset and find frequent item sets and generate

association rules for minimum support count is 2 minimum confidence is

60%

TID Items

T1 I1, I2, I5

T2 I2, I4

T3 I2, I3

T4 I1, I2, I4

T5 I1, I3

T6 I2, I3

T7 I1, I3

T8 I1, I2, I3, I5

T9 I1, I2, I3

Solution:

Step:1 Find the Frequent 1-itemset

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1-itemset Freq

I1 6

I2 7

I3 6

I4 2

I5 2

Step 2: Find the Frequent 2-itemset

2-itemset Freq

I1,I2 4

I1, I3 4

I1, I4 1

I1, I5 2

I2, I3 4

I2, I4 2

I2, I5 2

I3, I4 0

I3, I5 1

I4, I5 0

Step 3: Find the Frequent 3-itemset

3-itemset Freq

I1,I2, I3 2

I1, I2, I4 1

I1, I2, I5 2

I1, I3, I4 0

I1, I3, I5 1

I1, I4, I5 0

I2, I3, I4 0

I2, I3, I5 1

I2, I4, I5 0

I3, I4, I5 0

Step 4: Find the Frequent 4-itemset

4-itemset Freq

I1, I2, I3, I5 1

Frequent 3-itemsets

{I1, I2,I3}, {I1, I2, 5}

Strong Association Rules satisfying Minimum Confidence: