Go-Apriori is a simple go implementation of the Apriori algorithm for finding frequent sets and association rules

Apriori is a classic algorithm for learning association rules. Apriori is designed to operate on databases / data sets containing transactions (for example, collections of items bought by customers)

The algorithm extracts useful information from large amounts of data. For example, the information that a customer who purchases 'butter' also tends to buy 'jam' at the same time is acquired from the association rule below:

• Support: The percentage of task-relevant data transactions for which the pattern is true.

Support (Butter->Jam) = ( No of transactions containing both 'butter' and 'jam' ) / ( Total no of transactions ) 

• Confidence: The measure of certainty or trustworthiness associated with each discovered pattern.

Confidence (Butter->Jam) = ( No of transactions containing both 'butter' and 'jam' ) / ( No of transactions containing 'butter' ) 

• Lift: This measure of how likely item 'jam' is purchased when item 'butter' is purchased, while controlling for how popular item 'butter' is

Lift (Butter->Jam) = ( No of transactions containing both 'butter' and 'jam' ) / ( No of transactions containing 'butter' ) * ( No of transactions containing 'jam' ) 

The algorithm aims to find the rules which satisfy both a minimum support threshold and a minimum confidence threshold.

• Item: article in the basket.
• Itemset: a group of items purchased together in a single transaction.

• Find all frequent itemsets:
  • Get frequent items:
    • Items whose occurrence is greater than or equal to the minimum support threshold.
  • Get frequent itemsets:
    • Generate candidates from frequent items.
    • Prune the results to find the frequent itemsets.
• Generate association rules from frequent itemsets:
  • Rules which satisfy the minimum support, minimum confidence and minimum lift thresholds.

go get github.com/eMAGTechLabs/go-apriori 

type Options struct { minSupport float64 // The minimum support of relations (float). minConfidence float64 // The minimum confidence of relations (float). minLift float64 // The minimum lift of relations (float). maxLength int // The maximum length of the relation (integer). }

Note: If maxLength is set to 0, no max length will be taken into consideration

import "github.com/eMAGTechLabs/go-apriori" transactions := [][]string{ {"beer", "nuts", "cheese"}, {"beer", "nuts", "jam"}, {"beer", "butter"}, {"nuts", "cheese"}, {"beer", "nuts", "cheese", "jam"}, {"butter"}, {"beer", "nuts", "jam", "butter"}, {"jam"}, } apriori := NewApriori(transactions) results := apriori.Calculate(NewOptions(0.1, 0.5, 0.0, 0))

[ ... { supportRecord: {items: [beer cheese jam nuts] support:0.125 } orderedStatistic: [ { base: [beer cheese jam] add: [nuts] confidence: 1 lift: 1.6 } { base: [beer cheese nuts] add: [jam] confidence: 0.5 lift: 1 } { base: [cheese jam nuts] add: [beer] confidence: 1 lift: 1.6 } ] } ... ] 

• Association Rules and the Apriori Algorithm
• Apyori - Apriori python implementation
• Apriori-Algorithm - Apriori C# implementation

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#apriori-algorithm #go