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

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.

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.

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%

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

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

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

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

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

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

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

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”.

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

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.

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)

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

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

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])

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

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.

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 12-15 1994, Chile, 487-99, pdf, ISBN 1-55860-153-8. Mannila H, Toivonen H, Verkamo AI. "Efficient algorithms for discovering association rules." AAAI Workshop on Knowledge Discovery in Databases (SIGKDD). July 1994, Seattle, 181-92, ps. Implementation of the algorithm in C# Retrieved from "http://en.wikipedia.org/wiki/Apriori_algorithm"