1. Overview Definition of Apriori Algorithm
Steps to perform Apriori Algorithm
Apriori Algorithm Examples
Pseudo Code for Apriori Algorithm
Apriori Advantages/Disadvantages
References

2. Definition of Apriori Algorithm
In computer science and data mining, Apriori is a classic algorithm for learning association rules.
Apriori is designed to operate on databases containing transactions (for example, collections of items bought by customers, or details of a website frequentation).
The algorithm attempts to find subsets which are common to at least a minimum number C (the cutoff, or confidence threshold) of the itemsets.

3. Definition (contd.)
Apriori uses a "bottom up" approach, where frequent subsets are extended one item at a time (a step known as candidate generation, and groups of candidates are tested against the data.
The algorithm terminates when no further successful extensions are found.
Apriori uses breadth-first search and a hash tree structure to count candidate item sets efficiently.

4. Apriori Algorithm Examples Problem Decomposition
If the minimum support is 50%, then {Shoes, Jacket} is the only 2- itemset that satisfies the minimum support.
If the minimum confidence is 50%, then the only two rules generated from this 2-itemset, that have confidence greater than 50%, are:
Shoes  Jacket Support=50%, Confidence=66%
Jacket  Shoes Support=50%, Confidence=100%

5. The Apriori Algorithm — Example
Min support =50%
Database D C1 L1
Scan D C2 C2 L2
Scan D C3 L3
Scan D

6. Apriori Advantages/Disadvantages
Uses large itemset property
Easily parallelized
Easy to implement
Disadvantages
Assumes transaction database is memory resident.
Requires many database scans.

7. Market Basket Analysis
Categorize customer purchase behavior
identify actionable information
purchase profiles
profitability of each purchase profile
use for marketing
layout or catalogs
select products for promotion
space allocation, product placement

8. Market Basket Analysis
Steve Schmidt - president of ACNielsen-US
Market Basket Benefits
selection of promotions, merchandising strategy
sensitive to price: Italian entrees, pizza, pies, Oriental entrees, orange juice
uncover consumer spending patterns
correlations: orange juice & waffles
joint promotional opportunities

9. Market Basket Analysis
Retail outlets
Telecommunications
Banks
Insurance
link analysis for fraud
Medical
symptom analysis

10. Market Basket Analysis
Chain Store Age Executive (1995)
1) Associate products by category
2) what % of each category was in each market basket
Customers shop on personal needs, not on product groupings

11. Possible Market Baskets
Customer 1: beer, pretzels, potato chips, aspirin
Customer 2: diapers, baby lotion, grapefruit juice, baby food, milk
Customer 3: soda, potato chips, milk
Customer 4: soup, beer, milk, ice cream
Customer 5: soda, coffee, milk, bread
Customer 6: beer, potato chips

12. Market Basket Analysis with R
Association Rules
There are many ways to see the similarities between items. These are techniques that fall under the general umbrella of association.
The outcome of this type of technique, in simple terms, is a set of rules that can be understood as “if this, then that”.

13. Applications There are many applications of association:
Product recommendation – like Amazon’s “customers who bought that, also bought this”
Music recommendations – like Last FM’s artist recommendations
Medical diagnosis – like with diabetes really cool stuff
Content optimisation – like in magazine websites or blogs

14. Key Terms
Support: The fraction of which our item set occurs in our dataset.
Confidence: probability that a rule is correct for a new transaction with items on the left.
Lift: The ratio by which by the confidence of a rule exceeds the expected confidence.  Note: if the lift is 1 it indicates that the items on the left and right are independent.

15. Apriori Recommendation with R
loading up our libraries and data set.
# Load the libraries
library(arules)
library(arulesViz)
library(datasets)  
# Load the data set
data(Groceries)

16. Explore the data before we make any rules:
# Create an item frequency plot for the top 20 items
temFrequencyPlot(Groceries,topN=20,type="absolute")

18. support and confidence
You will always have to pass the minimum required support and confidence.
We set the minimum support to 0.001
We set the minimum confidence of 0.8
We then show the top 5 rules

19. Get & Inspect Rules # Get the rules
rules <- apriori(Groceries, parameter = list(supp = 0.001, conf = 0.8))  
# Show the top 5 rules, but only 2 digits options(digits=2) inspect(rules[1:5])

20. Result
This reads easily, for example: if someone buys yogurt and cereals, they are 81% likely to buy whole milk too.

21. Summary Association Rules form an very applied data mining approach.
Association Rules are derived from frequent itemsets.
The Apriori algorithm is an efficient algorithm for finding all frequent itemsets.
The Apriori algorithm implements level-wise search using frequent item property.
The Apriori algorithm can be additionally optimized.
There are many measures for association rules.

22. References References
Agrawal R, Imielinski T, Swami AN. "Mining Association Rules between Sets of Items in Large Databases." SIGMOD. June 1993, 22(2):207-16, pdf.
Agrawal R, Srikant R. "Fast Algorithms for Mining Association Rules", VLDB. Sep , Chile, , pdf, ISBN
Mannila H, Toivonen H, Verkamo AI. "Efficient algorithms for discovering association rules." AAAI Workshop on Knowledge Discovery in Databases (SIGKDD). July 1994, Seattle, , ps.
Implementation of the algorithm in C#
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