This document discusses association rule learning and frequent pattern mining. It begins with an introduction to association rule mining using a grocery store example. It then describes the Apriori algorithm for finding frequent itemsets and generating association rules. The algorithm works in two steps - first finding all frequent itemsets whose support is above a minimum threshold, and then generating association rules from those itemsets where the confidence is above a minimum. An example run of the Apriori algorithm on a transactional database is shown. Finally, some potential application areas for association rule mining are discussed.

1. Association Rules and
Frequent Pattern
Analysis
Dr. Iqbal H. Sarker
Dept of CSE, CUET
Research LAB Web:
Sarker DataLAB
(http://sarkerdatalab.com/)
Machine Learning Slide 1
Iqbal H. Sarker

2. Today’s Agenda
 Introduction to Association Rules
 Motivation with Examples
 Algorithms
 How it works?
 Real life Application Areas
 Summary
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Iqbal H. Sarker Machine Learning

3. Introduction to AR
 Ideas come from the market basket analysis (MBA)
◼ Let’s go shopping!
Milk, eggs, sugar,
bread
Eggs, sugar
Milk, eggs, cereal,
bread
Customer1
Customer2 Customer3
◼ What do my customer buy? Which product are bought together?
◼ Aim: Find associations and correlations between the different
items that customers place in their shopping basket
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4. Association rule learning is
a rule-based machine
learning method for discovering
interesting relations between
variables in large databases.
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5. Real-Life Applications
Used in many recommender systems
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Machine Learning
Iqbal H. Sarker

6. Introduction to AR
 Formalizing the problem a little bit
◼ Transaction Database T: a set of transactions T = {t1, t2, …, tn}
◼ Each transaction contains a set of items I (item set)
◼ An itemset is a collection of items I = {i1, i2, …, im}
 General aim:
◼ Find frequent/interesting patterns, associations, correlations, or
causal structures among sets of items or elements in
databases or other information repositories.
◼ Put this relationships in terms of association rules
➢ X  Y
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7. What’s an Interesting Rule?
 An association rule is an TID Items
implication of two itemsets
◼ X  Y
T1
T2
T3
T4
T5
bread, jelly, peanut-butter
bread, peanut-butter
bread, milk, peanut-butter
beer, bread
beer, milk
 Many measures of interest.
The two most used are:
◼ Support (s)
➢ The occurring frequency of the rule,
i.e., number of transactions that
contain both X and Y
s =
(X Y)
No.of trans.
◼ Confidence (c)
➢ The strength of the association,
i.e., measures of how often items in Y
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Iqbal H. Sarker Machine Learning
appear in transactions that contain X
c =
(X  Y )
(X)

8. 8
Mining Association Rules—an Example
For rule A  C:
support = support({A}{C}) = 50%
confidence = support({A}{C})/support({A}) = 66.6%
Min. support 50%
Min. confidence 50%
Transaction-id Items bought
10 A, B, C
20 A, C
30 A, D
40 B, E, F
Frequent pattern Support
{A} 75%
{B} 50%
{C} 50%
{A, C} 50%
Machine Learning
Iqbal H. Sarker

9. The Apriori Algorithm: Basics
 The name, Apriori, is based on the fact that the algorithm
uses prior knowledge of frequent itemset properties
 It consists of two steps
1. Generate all frequent itemsets whose support ≥
minsup
2. Use frequent itemsets to generate association rules
 So, let’s pay attention to the first step
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Iqbal H. Sarker Machine Learning

10. Apriori
null
A B C D E
AB AD
AC AE BD
BC BE CE
CD DE
ABC ABE
ABD ACD ADE
ACE BCD BDE
BCE CDE
ABCD ABCE ABDE ACDE BCDE
ABCDE
Given n items, we have 2^n possible itemsets.
◼ Do we have to generate them all?
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11. Apriori
 Let’s avoid expanding all the graph
 Key idea:
◼ Use Apriori Property: Any subsets of a frequent itemset are
also frequent itemsets
 Therefore, the algorithm iteratively does:
◼ Create itemsets
◼ Only continue exploration of those whose support ≥ minsup
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12. Apriori: Pseudo-code
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Join Step: Ck is generated by joining Lk-1with itself
Prune Step: Any (k-1)-itemset that is not frequent cannot be a subset of a
frequent k-itemset
Pseudo-code:
Ck: Candidate itemset of size k
Lk : frequent itemset of size k
L1 = {frequent items};
for (k = 1; Lk !=; k++) do begin
Ck+1 = candidates generated from Lk;
for each transaction t in database do
increment the count of all candidates in Ck+1 that are contained in t
Lk+1 = candidates in Ck+1 with min_support
end
return k Lk;

13. Illustration of the Apriori
principle
Found to be
Infrequent
null
AB AC AD AE BC BD BE CD CE DE
A B C D E
ABC ABD ABE ACD ACE ADE BCD BCE BDE CDE
ABCD ABCE ABDE ACDE BCDE
ABCDE
null
AB AC AD AE BC BD BE CD CE DE
A B C D E
ABC ABD ABE ACD ACE ADE BCD BCE BDE CDE
ABCD ABCE ABDE ACDE BCDE
ABCDE
Pruned
Infrequent supersets

14. Another Example
null
Infrequent
itemset
A B C D E
AB AD
AC AE BD
BC BE CE
CD DE
ABC ABE
ABD ACD ADE
ACE BCD BDE
BCE CDE
ABCD ABCE ABDE ACDE BCDE
ABCDE
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15. Example of Apriori Run
Database TDB
L1
C1
1st scan
C2 C2
L2 2nd scan
L3
C3 3rd scan
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Machine Learning
Itemset sup
{B, C, E} 2
Itemset
{B, C, E}
Itemset sup
{A, C} 2
{B, C} 2
{B, E} 3
{C, E} 2
Itemset
{A, B}
{A, C}
{A, E}
{B, C}
{B, E}
{C, E}
Itemset sup
{A, B} 1
{A, C} 2
{A, E} 1
{B, C} 2
{B, E} 3
{C, E} 2
Tid Items
10 A, C, D
20 B, C, E
30 A, B, C, E
40 B, E
Itemset sup
{A} 2
{B} 3
{C} 3
{E} 3
Itemset sup
{A} 2
{B} 3
{C} 3
{D} 1
{E} 3
Machine Learning Slide 15

16. Apriori
 Remember that Apriori consists of two steps
1. Generate all frequent itemsets whose support ≥ minsup
2. Use frequent itemsets to generate association rules
 We accomplished step 1. So we have all frequent
itemsets
 So, let’s pay attention to the second step
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17. Rule Generation in Apriori
 Given a frequent itemset L
◼ Find all non-empty subsets F in L, such that the association
rule F  {L-F} satisfies the minimum confidence
◼ Create the rule F  {L-F}
 If L={A,B,C}
◼ The candidate itemsets are: ABC, ACB, BCA, ABC,
BAC, CAB
◼ In general, there are 2K-2 candidate solutions, where k is the
length of the itemset L
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18. Example of Efficient Rule Generation
ABCD
Low
confidence
ABCD ABDC ACDB BCDA
ABCD ACBD BCAD BDAD
ADBC CDAB
ABCD BACD CABD DABC
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Pruned
Rules

19. Relevant Algorithms
1. Apriori
2. FP-Growth
3. ECLAT
4. ABC-RuleMiner (Sarker et al., Elsevier)
[ABC-RuleMiner: User behavioral rule-based machine learning method for
context-aware intelligent services, Journal of Network and Computer
Applications, Elsevier, 2020]
5. Others…
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20. Possible Application Areas
➢Market Basket Analysis
➢Context-Aware Intelligent Systems
➢Medical Diagnosis
➢Mobile Applications
➢Smart Cities
➢Cyber security
➢Protein Sequence
➢Web Usage
➢Census Data
➢So on..
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21. Questions ?
Thank You !!!
Sarker DataLAB
(http://sarkerdatalab.com/)
Email: iqbal.sarker.cse@gmail.com
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