**Week 1 Quiz Answer**

### Lesson 1 Quiz

**Question 1)**

**Table 1: Transactions from a database**

**Given the transactions in Table 1, mini-support (minsup) s = 50%,**

which of the following isnot a frequent itemset?

which of the following isnot a frequent itemset?

**{Coffee}**- {Beer}
- {Eggs}
- {Beer, Diapers}

**Question 2)**

**Table 1: Transactions from a database**

**Given the transactions in Table 1, what is the confidence and**

relative support of the association rule {Diapers} ⇒ {Coffee,

Nuts}?

relative support of the association rule {Diapers} ⇒ {Coffee,

Nuts}?

**support s = 0.4, confidence c = 0.5**- support s = 0.8, confidence c = 0.5
- support s = 0.4, confidence c = 1
- support s = 0.8, confidence c = 1
- None of the above

**Question 3)**

**Consider the database containing the transaction T1 : {a1, a2, a3},**

T2 : {a2, a3, a4}, T3 : {a1,a3, a4}. Let mini-support (minsup) = 2.

Which of the following frequent patterns is closed?

T2 : {a2, a3, a4}, T3 : {a1,a3, a4}. Let mini-support (minsup) = 2.

Which of the following frequent patterns is closed?

- {a2}
**{a1}**- {a1, a3}
- {a4}

**Question 4)**

**Consider the database containing the transactions T1 : {a1, …, a3},**

T2 : {a2, …, a4}. Letminsup = 1. What fraction of all frequent

patterns is max frequent patterns?

T2 : {a2, …, a4}. Letminsup = 1. What fraction of all frequent

patterns is max frequent patterns?

- 1/11
**2/11**- 1/3
- There are no max frequent patterns for the given minsup.
- 3/11

**Question 5)**

**Rank the following sets by their cardinality for a given database:**

{all frequent patterns}, {closed frequent patterns}, and {max frequent

patterns}.

{all frequent patterns}, {closed frequent patterns}, and {max frequent

patterns}.

- {all frequent patterns} ≥ {closed frequent patterns} ≥ {max frequent

patterns}

- {all frequent patterns} ≥ {max frequent patterns} ≥ {closed frequent

patterns}

- {all frequent patterns} ≥ {max frequent patterns} = {closed

frequent patterns}, i.e. the set of max frequent patterns and the

set of closed frequent patterns are identical.

- {all frequent patterns} ≥ {max frequent patterns}, {all frequent

patterns} ≥ {closed frequent patterns}, but the order of {max

frequent patterns} and {closed frequent patterns} cannot be

determined without further information.

- Ranking is impossible without further information.

**Question 6)**

**Table 1: Transactions from a database**

**Given the transaction in Table 1 and mini-support (minsup) s = 40%,**

which of the following is a length-3 frequent item set?

which of the following is a length-3 frequent item set?

- Beer, Nuts, Eggs
- Beer, Coffee, Milk
- Coffee, Diapers, Eggs
**Beer, Nuts, Diapers**

**Question 7)**

**A strong association rule satisfies both the mini-support (minsup)**

and minconfthresholds. Given the transactions in Table 1, mini-support

(minsup)s = 50%, andminconf c = 50%, which of the following is not a

strong association rule?

and minconfthresholds. Given the transactions in Table 1, mini-support

(minsup)s = 50%, andminconf c = 50%, which of the following is not a

strong association rule?

- {Beer} ⇒ {Diapers}
**{Beer, Nuts} ⇒ {Diapers}**- {Diapers} ⇒ {Nuts}
- {Nuts} ⇒ {Diapers}
- {Diapers} ⇒ {Beer}

**Question 8)**

**Consider the database containing the transaction T1 : {a1, a2, a3},**

T2 : {a2, a3, a4}, T3 : {a1,a3, a4}. Let mini-support (minsup) = 2.

Which of the following frequent patterns is NOT closed?

T2 : {a2, a3, a4}, T3 : {a1,a3, a4}. Let mini-support (minsup) = 2.

Which of the following frequent patterns is NOT closed?

- {a2}
- {a1, a3}
- {a3}
**{a3, a4}**

**Question 9)**

**Consider the database containing the transactions T1 : {a1, a2, a3,**

a4, a5}, T2 : {a2, a3, a4, a5,a6}. Let minsup = 1. Which of the

following is both a max frequent and a closed frequent pattern? Select

all that apply.

a4, a5}, T2 : {a2, a3, a4, a5,a6}. Let minsup = 1. Which of the

following is both a max frequent and a closed frequent pattern? Select

all that apply.

- {a2, a3, a4, a5}
- {a2, a5}
**{a1, a2, a3, a4, a5}****{a2, a3, a4, a5, a6}**- {a1, a2, a3, a4, a5, a6}

**Question 10)**

**Given the set of closed frequent patterns, we can ___________. Select**

all that apply.

all that apply.

- Recover all transactions in the database
**Find the set of max frequent patterns**- Recover the set of all frequent patterns and their support in some

situations but not all

Always recover the set of all frequent patterns and their support

**Question 11)**

**Given the transactions in Table 1, mini-support (minsup) s= 50%, and**

minconf c = 50%, which of the following is an association rule? Select

all that apply.

minconf c = 50%, which of the following is an association rule? Select

all that apply.

- Nuts ⇒ Eggs
- Coffee ⇒ Milk
- Diapers ⇒ Eggs
- Nuts ⇒ Diapers
- Beer ⇒ Nuts

**Question 12)**

**Which of the following statements is true?**

- The set of closed frequent patterns is always the same as the set of

max frequent patterns.

- Since both closed and max frequent patterns are a subset of all

frequent patterns, we cannot recover all frequent patterns and their

supports given just the closed and max frequent patterns.

- Closed frequent patterns can always be determined from the set of

max frequent patterns.

- We can recover all frequent patterns and their supports from the set

of max frequent patterns.

**We can recover all frequent patterns and their supports from the**

set of closed frequent patterns.

**Question 13)**

**Given the transactions in Table 1, what is the confidence and**

relative support of the association rule {Diapers} ⇒ {Coffee,

Nuts}?

relative support of the association rule {Diapers} ⇒ {Coffee,

Nuts}?

**support s = 0.4, confidence c = 0.5**- support s = 0.8, confidence c = 0.5
- support s = 0.4, confidence c = 1
- support s = 0.8, confidence c = 1
- None of the above

**Lesson 2 Quiz**

**Question 1)**

**If we know the support of itemset {a, b} is 10, which of the**

following numbers are the possible supports of itemset {a, b, c}?

Select all that apply.

following numbers are the possible supports of itemset {a, b, c}?

Select all that apply.

- 11
**9****10**

**Question 2)**

**If we know the support of itemset {a} is 50 and the support of**

itemset {a, b, c} is 30, which of the following numbers are the

possible supports of itemset {a, b}? Select all that apply.

itemset {a, b, c} is 30, which of the following numbers are the

possible supports of itemset {a, b}? Select all that apply.

**10**- 5
**30**- 100
- 50

**Question 3)**

**Considering the Apriori algorithm, assume we have obtained all size-2**

(i.e., containing 2 items, e.g. {A, B}) frequent itemsets. They are

{A, B}, {A, C}, {A, D}, {B, C}, {B, E}, and {C, E}. In the following

size-3 itemsets, which of them should be considered, i.e., have

potential to be size-3 frequent itemsets? Select all that apply.

(i.e., containing 2 items, e.g. {A, B}) frequent itemsets. They are

{A, B}, {A, C}, {A, D}, {B, C}, {B, E}, and {C, E}. In the following

size-3 itemsets, which of them should be considered, i.e., have

potential to be size-3 frequent itemsets? Select all that apply.

- {A, B, D}
- {A, C, D}
**{B, C, E}**- {A, B, C}

**Question 4)**

**Given the FP-tree as shown in Figure 1, how many transactions do we**

have in total?

have in total?

- 4
**5**- 3
- 1
- 2

**Question 5)**

**If we know the support of itemset {a} is 50 and the support of**

itemset {a, b, c} is 10, which of the following numbers are the

possible supports of itemset {a, b}? Select all that apply.

itemset {a, b, c} is 10, which of the following numbers are the

possible supports of itemset {a, b}? Select all that apply.

- 5
**10****50****30**- 100

**Question 6)**

**Considering the Apriori algorithm, assume we have 5 items (A to E) in**

total. In the 1st scan, we find out all frequent items A, B, C, and E.

How many size-2 (i.e., containing 2 items, e.g. A, B) itemsets should

be considered in the 2nd scan, i.e., have potential to be size-2

frequent itemsets? Select all that apply.

total. In the 1st scan, we find out all frequent items A, B, C, and E.

How many size-2 (i.e., containing 2 items, e.g. A, B) itemsets should

be considered in the 2nd scan, i.e., have potential to be size-2

frequent itemsets? Select all that apply.

- 10
- 25
- 4
**6**

**Question 7)**

**Given the FP-tree as shown Figure 1, which of the following choices**

is in the f-conditional database? Select all that apply.

is in the f-conditional database? Select all that apply.

**{c, a, b, m} : 1**- {c, b, p} : 1
**{b} : 1****{c, a, m, p} : 2**

**Extra Question**

**Question 1)**

**Which of the following tasks does not fall under the scope of data**

mining? Select all that apply.

mining? Select all that apply.

**Data entry.****Data Cleaning.**

**Question 2)**

**Given the transaction in Table 1 and minsup s = 50%, how many**

frequent 3-itemsets are there?

frequent 3-itemsets are there?

**Answer:**

**0**

**Question 3)**

**A strong association rule satisfies both the minsup and minconf**

thresholds. Given the transactions in Table 1, minsup s = 50%, and

minconf c = 50%, how many strong association rules are there? Note

that the association rule A => B and B => A are distinct.

thresholds. Given the transactions in Table 1, minsup s = 50%, and

minconf c = 50%, how many strong association rules are there? Note

that the association rule A => B and B => A are distinct.

**Answer:**

**6**

**Question 4)**

**Given the transactions in Table 1, minsup s = 50%, and minconf c =**

50%, which of the following is an association rule? Select all that

apply.

50%, which of the following is an association rule? Select all that

apply.

**Answer:**

**Beer => Nuts****Nuts => Diaper**

**Question 5)**

**Consider the database containing the transaction T1 : {a1, …, a5},**

T2 : {a1, …, a1}, T3 : {a3, …, a7}, T4 : {a4, …, a8}. For what

value of minsup do we have the most number of closed frequent

patterns?

T2 : {a1, …, a1}, T3 : {a3, …, a7}, T4 : {a4, …, a8}. For what

value of minsup do we have the most number of closed frequent

patterns?

**Answer:**

**minsup = 1**

**Question 6)**

**Consider the database containing the transactions T1 : {a1, …, a3},**

T2 : {a2, …, a4}. Let minsup = 1. What fraction of all frequent

patterns is max frequent patterns?

T2 : {a2, …, a4}. Let minsup = 1. What fraction of all frequent

patterns is max frequent patterns?

**Answer:**

**2/11**

**Question 7)**

**Consider the database containing the transaction T1 : {a1, a2, a3},**

T2 : {a2, a3, a4}. Let minsup = 1. What fraction of all frequent

patterns is closed?

T2 : {a2, a3, a4}. Let minsup = 1. What fraction of all frequent

patterns is closed?

**Answer:**

**3/11**

**Question 8)**

**Rank the following sets by their cardinality for a given database:**

{all frequent patterns}, {closed frequent patterns}, {max frequent

patterns}

{all frequent patterns}, {closed frequent patterns}, {max frequent

patterns}

**Answer:**

**{all frequent patterns} >= {closed frequent patterns} >= {max**

frequent patterns}

**Question 9)**

**Which of the following statements is true?**

**Answer:**

**We can recover all frequent patterns from the set of closed**

frequent patterns.

**Question 10)**

**If we know the support of itemset {a, b, c} is 10, which of the**

following numbers are the possible supports of the itemset {a, b}?

following numbers are the possible supports of the itemset {a, b}?

**Answer:**

**10****11**

**Question 11)**

**If we know the support of itemset {a, b} is 10, which of the**

following numbers are the possible supports of itemset {a, b, c}?

following numbers are the possible supports of itemset {a, b, c}?

**Answer:**

**9****10**

**Question 12)**

**If we know the support of itemset {a} is 50, and the support of**

itemset {a, b, c} is 10, which of the following numbers are the

possible supports of itemset {a, d}?

itemset {a, b, c} is 10, which of the following numbers are the

possible supports of itemset {a, d}?

**Answer:**

**5****50****30****10**

**Question 13)**

**Considering Apriori Algorithm, assume we have 5 items (A to E) in**

total. In the 1-st scan, we find out all frequent items A, B, C, and

E. How many size-2 (i.e. containing 2 items, e.g. A, B) itemsets

should be considered in 2-nd scan, i.e. are potential to be size-2

frequent itemsets?

total. In the 1-st scan, we find out all frequent items A, B, C, and

E. How many size-2 (i.e. containing 2 items, e.g. A, B) itemsets

should be considered in 2-nd scan, i.e. are potential to be size-2

frequent itemsets?

**Answer:**

**6**

**Question 14)**

**Considering Apriori Algorithm, assume we have obtained all size-2**

(i.e. containing 2 items, e.g. {A, B}) frequent itemsets. They are {A,

B}, {A, C}, {A, D}, {B, C}, {B, E}, {C, E}. In the following size-3

itemsets, which of them should be considered, i.e. are potential to be

size-3 frequent itemsets?

(i.e. containing 2 items, e.g. {A, B}) frequent itemsets. They are {A,

B}, {A, C}, {A, D}, {B, C}, {B, E}, {C, E}. In the following size-3

itemsets, which of them should be considered, i.e. are potential to be

size-3 frequent itemsets?

**Answer:**

**{A, B, C}****{B, C, E}**

**Question 15)**

**Given the FP-tree as shown in Figure 1, what is the support of {c,**

p}?

p}?

**Answer:**

**3**

Pattern Discovery in Data Mining

**Week 2 Quiz Answer**

### Lesson 3 Quiz

**Question 1)**

**What is the value range of the Kulczynski measure?**

- (-∞, +∞)
- [-1, 1]
**[0, 1]**- [0, +∞)

**Question 2)**

**What is the value range of the χ2 measure?**

- (-∞, +∞)
- [-1, 1]
- [0, 1]
- [0, +∞)

**Question 6)**

**Which of the following measures is NOT null invariant?**

- Cosine
- Lift
- All confidence
**Kulcyzynski**

**Question 7)**

**Suppose we are interested in analyzing the purchase of comics**

(CM) and fiction (FC) in the transaction history of a bookstore. We have

the following 2 × 2 contingency table summarizing the transactions. If

χ2 is used to measure the correlation between CM and FC, what is the χ2

score?

(CM) and fiction (FC) in the transaction history of a bookstore. We have

the following 2 × 2 contingency table summarizing the transactions. If

χ2 is used to measure the correlation between CM and FC, what is the χ2

score?

- -240
- -80
- 80
**240**

**Question 7)**

**What is the value range of the Kulczynski measure?**

- [0, 1]
- (-∞, +∞)
- [-1, 1]
- [0, +∞)

**Question 10)**

**Suppose we are interested in analyzing the purchase of comics (CM) and**

fiction (FC) in the transaction history of a bookstore. We have the

following 2 × 2 contingency table summarizing the transactions. If lift

is used to measure the correlation between CM and FC, what is the value

for lift(CM, FC)?

fiction (FC) in the transaction history of a bookstore. We have the

following 2 × 2 contingency table summarizing the transactions. If lift

is used to measure the correlation between CM and FC, what is the value

for lift(CM, FC)?

- -0.6
**0.6**- -2e-4
- 2e-4

**Questine 11)**

**Suppose we are interested in analyzing the transaction history of**

several supermarkets with respect to purchase of apples (A) and bananas

(B). We have the following table summarizing the transactions.

several supermarkets with respect to purchase of apples (A) and bananas

(B). We have the following table summarizing the transactions.

**Which of the following measures would you use to determine the**

correlation of purchases between apples and bananas across all these

supermarkets?

correlation of purchases between apples and bananas across all these

supermarkets?

- χ2
**Kulcyzynski**- Lift
- Cosine

**Question 12)**

**Suppose a school collected some data on students’ preference for hot**

dogs (HD) vs. hamburgers (HM). We have the following 2×2 contingency

table summarizing the statistics. If χ2 is used to measure the

correlation between HD and HM, what is the χ2score?

dogs (HD) vs. hamburgers (HM). We have the following 2×2 contingency

table summarizing the statistics. If χ2 is used to measure the

correlation between HD and HM, what is the χ2score?

- 0
- -1
- -∞
- 1

### Lesson 4 Quiz

**Question 1)**

**Suppose one needs to frequent patterns at two different levels, with**

mini-support (minsup) of 5% (higher level) and 3% (lower level),

respectively. If using shared multi-level mining, which mini-support

(minsup) threshold should be used to generate candidate patterns for

the higher level?

mini-support (minsup) of 5% (higher level) and 3% (lower level),

respectively. If using shared multi-level mining, which mini-support

(minsup) threshold should be used to generate candidate patterns for

the higher level?

**3%**- 1%
- 8%
- 5%

**Question 2)**

**A store had 100,000 total transactions in Q4 2014. 10,000**

transactions contained eggs, while 5,000 contained bacon. 2000

transactions contained both eggs and bacon. Which of the following

choices for the value of ε is the smallest such that {eggs, bacon} is

considered a negative pattern under the null-invariant definition?

transactions contained eggs, while 5,000 contained bacon. 2000

transactions contained both eggs and bacon. Which of the following

choices for the value of ε is the smallest such that {eggs, bacon} is

considered a negative pattern under the null-invariant definition?

- 0.1
- 0.81
- 0.5
- 01
- A value for ε such that {eggs, bacon} is a negative pattern under

the null-invariant definition does not exist.

**Question 3)**

**Below is a table of transactions. According to the introduced pattern**

distance measure, what is the distance between pattern “abc” and

pattern “abd”?

distance measure, what is the distance between pattern “abc” and

pattern “abd”?

- 0
- 0.5
- 0.2
- 0.333

**Question 4)**

**Given the itemsets in Table 1 and a cluster quality measure δ =**

0.001, what could be a set of representative patterns that covers all

itemsets in Table 1?

0.001, what could be a set of representative patterns that covers all

itemsets in Table 1?

which pattern in the table may δ-cover the pattern {F, A, C, E, T, S}.

- {{F, A, C, E, T, S}}

- {{F, A, C, E, S}, {A, C, E, S}}

- {{F, A, C, E, S}, {F, A, C, T, S}}

- {{F, A, C, E, S}, {F, A, C, E, T, S}, {F, A, C, T, S}}

- {{A, C, E, S}, {A, C, T, S}}

**Question 5)**

**A store had 100,000 total transactions in Q4 2014. 10,000**

transactions contained beer, while 5,000 contained frying pans. 600

transactions contained both beer and frying pans. Which of the

following is true?

transactions contained beer, while 5,000 contained frying pans. 600

transactions contained both beer and frying pans. Which of the

following is true?

- More information is needed to determine if {beer, frying pans} is a

negative pattern.

- {beer, frying pans} is a negative pattern under the support-based

definition of negatively correlated patterns.

**For ε = 0.1, {beer, frying pans} is a negative pattern under the**

null-invariant definition of negatively correlated patterns.

- There does not exist a value for ε such that {beer, frying pans} is

a negative pattern by the null-invariant definition of negative

patterns.

**Question 6)**

**Given the itemsets in Table 1, which of the following patterns are in**

the δ-cluster containing the pattern {A, C, E, S} for δ = 0.0001?

the δ-cluster containing the pattern {A, C, E, S} for δ = 0.0001?

O(Pi) is the corresponding itemset of pattern Pi . Take a second to

convince yourself that the following is true:

- {A, C, T, S}

**{F, A, C, E, S}**

- {F, A, C, T, S}

- {F, A, C, E, T, S}

**Question 7)**

**Consider two patterns P1 and P2 such that O(P1) ⊆ O(P2), where O(Pi)**

is the corresponding itemset of pattern Pi. Take a second to convince

yourself that the following is true:

is the corresponding itemset of pattern Pi. Take a second to convince

yourself that the following is true:

**Which of the following patterns in Table 1 is δ-covered by {F, A, C,**

E, T, S} for δ=0.4? Select all that apply.

E, T, S} for δ=0.4? Select all that apply.

- {A, C, E, S}

- {F, A, C, T, S}

- {A, C, T, S}

- {F, A, C, E, S}

### Extra Questions

**Question 1)**

**Suppose a school collected some data on students’ preference for**

hot dogs(HD) vs. hamburgers (HM). We have the following 2×2

contingency table summarizing the statistics. If lift is used to

measure the correlation between HD and HM, what is the value for

lift(HD, HM)?

hot dogs(HD) vs. hamburgers (HM). We have the following 2×2

contingency table summarizing the statistics. If lift is used to

measure the correlation between HD and HM, what is the value for

lift(HD, HM)?

**Answer:**

**1**- -∞
- 0
- -1

**Question 2)**

**Suppose Coursera collected statistics on the number of students**

who take courses on data mining (DM) and machine learning (ML). We

have the following 2×2 contingency table summarizing the

statistics. If χ2 is used to measure the correlation between DM

and ML, what is the χ2 score?

who take courses on data mining (DM) and machine learning (ML). We

have the following 2×2 contingency table summarizing the

statistics. If χ2 is used to measure the correlation between DM

and ML, what is the χ2 score?

**Answer:**

**562.5**- -562.5
- -225
- 225

**Question 3)**

**What is the value range of the Lift measure?**

**Answer:**

**ric: normal; vertical-align: baseline; white-space: pre-wrap;”>[0, +∞)**- [0, 1]
- (-∞, +∞)
- [-1, 1]

**Question 4)**

**Which of the following measures is NOT null invariant?**

**Answer:**

**X**2

**Question 5)**

**Suppose we are interested in analyzing the transaction history of**

several supermarkets with respect to purchase of apples(A) and

bananas(B). We have the following table summarizing the

transactions.

several supermarkets with respect to purchase of apples(A) and

bananas(B). We have the following table summarizing the

transactions.

**Denote li as the lift measure and ki as the Kulcyzynski measure**

for supermarket Si(i = 1, 2). Which of the following is

correct?

for supermarket Si(i = 1, 2). Which of the following is

correct?

**Answer:**

**l1 ≠ l2, k1 = k2**