Node-Apriori

Apriori Algorithm implementation in TypeScript / JavaScript.

Getting Started

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This implementation does not generate k-candidates as efficiently as it possibly could, as it adopts a brute-force approach: Every k-itemset is considered as a potential candidate, and an additional step is required to prune the unnecessary ones. More information about this question here.

The Apriori Algorithm is a great, easy-to-understand algorithm for frequent-itemset mining. However, faster and more memory efficient algorithms such as the FPGrowth Algorithm have been proposed since it was released.

If you need a more efficient frequent-itemset mining algorithm, consider checking out my implementation of the FPGrowth Algorithm.

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This is a Node.js module available through the npm registry.

Installation is done using the npm install command:

``bash
$ npm install --save node-apriori
`

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`js

import { Apriori, Itemset, IAprioriResults } from 'node-apriori';

let transactions: number[][] = [
[1,3,4],
[2,3,5],
[1,2,3,5],
[2,5],
[1,2,3,5]
];

// Execute Apriori with a minimum support of 40%. Algorithm is generic.
let apriori: Apriori = new Apriori(.4);

// Returns itemsets 'as soon as possible' through events.
apriori.on('data', (itemset: Itemset) => {
// Do something with the frequent itemset.
let support: number = itemset.support;
let items: number[] = itemset.items;
});

// Execute Apriori on a given set of transactions.
apriori.exec(transactions)
.then( (result: IAprioriResults) => {
// Returns both the collection of frequent itemsets and execution time in millisecond.
let frequentItemsets: Itemset[] = result.itemsets;
let executionTime: number = result.executionTime;
});


``

License

This project is licensed under the MIT License - see the LICENSE file for details