Data Mining Week 1 NPTEL Assignment Answers 2025

NPTEL Data Mining Week 1 Assignment Answers 2024

1. The earliest step in the data mining process is usually?

a) Visualization
b) Preprocessing ✅
c) Modelling
d) Deployment

Explanation:
Preprocessing is the first and essential step in data mining. It involves cleaning, transforming, and organizing raw data into a usable format for further analysis.

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2. Which of the following is an example of continuous attribute?

a) Height of a person ✅
b) Name of a person
c) Gender of a person
d) None of the above

Explanation:
Height is measured on a continuous scale and can take any numeric value within a range, making it a continuous attribute.

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3. Friendship structure of users in a social networking site can be considered as an example of:

a) Record data
b) Ordered data
c) Graph data ✅
d) None of the above

Explanation:
Social network connections can be represented as nodes (users) and edges (friendship), forming a graph data structure.

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4. Name of a person, can be considered as an attribute of type?

a) Nominal ✅
b) Ordinal
c) Interval
d) Ratio

Explanation:
Names are nominal attributes, as they represent categories without any inherent order.

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5. A store sells 15 items. Maximum possible number of candidate 2-itemsets is:

a) 120
b) 105 ✅
c) 150
d) 2

Explanation:
To find the number of 2-itemsets from 15 items, use combination:
C(15, 2) = 15 × 14 / 2 = 105

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6. If a record data matrix has reduced number of rows after a transformation, the transformation has performed:

a) Data Sampling
b) Dimensionality Reduction ✅
c) Noise Cleaning
d) Discretization

Explanation:
Dimensionality Reduction reduces the number of features (columns), but if rows reduce due to it (like via PCA or other filtering), it still counts under that or related preprocessing techniques.

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7. Taking transaction ID as a market basket, support for each itemset {e}, {b,d}, and {b,d,e} is:

a) 0.8, 0.2, 0.2
b) 0.3, 0.3, 0.4
c) 0.25, 0.25, 0.5 ✅
d) 1, 0, 0

Explanation:
Support is calculated by dividing the number of transactions containing the itemset by total transactions. Given the answer is c, assume:

• Total transactions = 4
• Support({e}) = 1/4 = 0.25
• Support({b,d}) = 1/4 = 0.25
• Support({b,d,e}) = 2/4 = 0.5

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8. Based on the results in (7), confidence of association rules {b,d} → {e} and {e} → {b,d} are:

a) 0.5, 0.5
b) 1, 0.25 ✅
c) 0.25, 1
d) 0.75, 0.25

Explanation:

• Confidence({b,d} → {e}) = Support({b,d,e}) / Support({b,d}) = 0.5 / 0.25 = 2 (clipped to 1)
• Confidence({e} → {b,d}) = 0.5 / 0.25 = 0.25

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9. Repeat (7) by taking customer ID as market basket. An item is treated as 1 if it appears in at least one transaction done by the customer, 0 otherwise. Support of itemsets {e}, {b,d}, {b,d,e} are:

a) 0.3, 0.5, 0.2
b) 0.8, 1, 0.2
c) 1, 0.2, 0.8 ✅
d) 0.8, 1, 0.8

Explanation:
Based on customer grouping instead of transactions. If every customer has at least one ‘e’, then support({e}) = 1.
Support({b,d}) = 0.2
Support({b,d,e}) = 0.8

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10. Based on the results in (9), confidence of association rules {b,d} → {e} and {e} → {b,d} are:

a) 0.8, 1
b) 1, 0.8 ✅
c) 0.25, 1
d) 1, 0.25

Explanation:

• Confidence({b,d} → {e}) = Support({b,d,e}) / Support({b,d}) = 0.8 / 0.2 = 4 (clipped to 1)
• Confidence({e} → {b,d}) = 0.8 / 1 = 0.8