Week 10

Question 1

What is Association Rule Mining (ARM)?

Answer 1

A “rule”-based machine learning method

Question 2

What is the objective of ARM?

Answer 2

To discover interesting relations between variables in large DBs.

Question 3

Association Rule Mining aims to identify _____ rules discovered in DBs, using some measures of interestingness

Answer 3

strong

Question 4

T/F - Association Rule Mining assumes all data is numeric

Answer 4

False. ARM assumes all data is categorical

Question 5

T/F - ARM features good algorithms for numeric data

Answer 5

False

Question 6

What was Associated Rule Mining initially used for?

Answer 6

Market analysis, to determine how items purchased by customers are related

Question 7

A customer buys bread, there is an 80% chance they buy butter.
bread > butter

or

A customer buys {onions, potatoes} > buys{tomatoes}

What is this an example of?

Answer 7

Associated Rule Mining

It was useful for marketing activities like product promotion or product pricing

Question 8

Bread > Butter[20%, 45%]

What is the Antecedent, Consequent?
What is the Support, Confidence?

Answer 8

Antecedent = Bread
Consequent = Butter
Support = 20%
Confidence = 45%

Question 9

In a store where there are 100 total transactions, what does a support of 20% for bread mean?

Answer 9

It means 20 transactions contained bread

Question 10

With 20 transactions that contain bread there is a 45% confidence that the transaction also contained butter. How many transactions had butter?

Answer 10

9

Question 11

T/F - Recommendation systems like Amazon’s “also viewed” use ARM

Answer 11

True

Question 12

Here are 3 algorithms for association rule:

Apriori Algorithm
Elcat Algorithm
F.P. Growth Algorithm

Which one is the best?

Answer 12

Apriori

Question 13

What do we find with this equation?

of transactions where X appears / Total # of transactions

Answer 13

Support (X)

Question 14

To find this we divide Support(X,Y) with Support (X)

Answer 14

Confidence

Question 15

Algorithm for k products:

• User sets a minimum support criterion
• Generate a list of one-item sets that meet the support criterion
• Use the list of one-item sets to generate list of two-item sets
• Use the list of two-item sets to generate list of three-item sets
• Continue up through k-item set
• All item sets must meet the support criterion

What do we get by following this algo.?

Answer 15

The Apriori algo. for Generating Frequent Item sets

Question 16

What is the maximum Confidence value for a X > Y rule if the consequent and antecedent always occur together? What is the minimum?

Answer 16

1, 0

Question 17

What does Lift measure?

Answer 17

How often X and Y happen together vs how often they happen independently

Question 18

What does it mean if the lift is greater than 1?

Answer 18

There is a positive correlation

Question 19

What does it mean if the lift is less than 1?

Answer 19

There is a negative correlation

Question 20

What does it mean if the lift is 1?

Answer 20

There is no relationship

Question 21

Association rules produce rules based on associations between ______ from a ___________ of transactions

Answer 21

items from a database of transactions