1. Frequent patterns and Association Rules
CSCI N317 Computation for Scientific Applications
Unit Weka
Frequent patterns and Association Rules

2. Frequent Pattern Analysis
Frequent pattern: a pattern (a set of items, subsequences, etc.) that occurs frequently in a data set
Motivation: Finding inherent regularities in data
What products were often purchased together?— Beer and diapers?!
What are the subsequent purchases after buying a PC?
What kinds of DNA are sensitive to this new drug?
Market Basket Analysis - Analyze customers buying habits by finding associations between different items that are placed in their “shopping baskets”
Help design store layouts
Items frequently purchased together can be placed together to further encourage the sale of both items
Items are placed at opposite ends of the store may entice customers to pick up other items along the way
Help retailers plan which items to put on sale at a reduced price

3. Association Rules
Patterns can be represented in the form of association rules
E.g.
Computer =>antivirus_software [support = 2%, confidence = 60%]
- 2% of all the transactions under analysis show that computer and antivirus software are purchased together.
- 60% of customers who purchased a computer also bought the software

4. Basic Concepts: Frequent Patterns and Association Rules
TID
Items bought 10
I1, I2, I4, 20
I1, I3, I4, 30
I1, I4, I5, 40
I2, I5, I6, 50
I2, I3, I4, I5, I6,
Itemset I = {I1, …, Im}
Find all the rules A  B with minimum support and confidence
support, s, probability that a transaction contains A  B
confidence, c, conditional probability that a transaction having A also contains B
Customer
buys diaper
buys both
buys beer
Let supmin = 50%, confmin = 50%
Association rules:
I1  I4 (60%, 100%)
I4  I1 (60%, 75%)

5. Basic Concepts: Frequent Patterns and Association Rules
Itemset I = {I1, …, Im}
Let A and B be a set of items
An association rule is an implication of the form A=>B, where
and
Support(A=>B) =
Confidence(A=>B) =
Rules that satisfy both a minimum support threshold and a minimum confidence threshold are called strong
The occurrence frequency of an itemset is the number of transactions that contain the itemset, also called frequency, support count or count
If the support of an itemset satisfies the minimum support threshold, the itemset is a frequent itemset
Looking for rules with high support

6. Interestingness Measure: Correlations (Lift)
Strong rules are not necessarily interesting:
E.g. 10,000 transactions, 6,000 include computer games, 7,500 include videos, and 4,000 include both computer games and videos. Association rule generated:
buys(X, “computer games”) => buys(X, “videos”)
[support = 40%, confidence = 66%]
The rule is misleading as the probability of purchasing videos is 75%, which is even larger than 66%. In fact, computer games and videos are negatively associated because the purchase of one decreases the likelihood of purchasing the other.
Correlation measure can be used to augment the support-confidence framework
A => B [support, confidence, correlation]

7. Interestingness Measure: Correlations (Lift)
Measure of dependent/correlated events: lift
P(AU B): the likelihood of purchasing both
P(A)P(B):the likelihood if the two purchases are independent
lift >1: A and B are positively correlated
lift<1: A and B are negatively correlated
lift = 1: A and B are independent
Looking for a rule with lift > 1

8. Interestingness Measure: Correlations (Lift)
Contingency table:
lift is smaller than 1. Two times are negatively correlated, thus the rule is not interesting.
Computer Game
No Computer Game
Total
Video Game
4,000
3,500
7,500
No Video Game
2,000
500
2,500
6,000
10,000

9. Association Rule Mining in R and Weka R
Need to install “arules” package when following examples on the video.
May need to install package from local zip file.
Sample code: dataMiningExample.R