simple-apriori/Apriori.js

Simple Apriori adalah library simple yang menerapkan metode Aprirori

'use strict';

const KoleksiItemset = require('./KoleksiItemset')
const Itemset = require('./Itemset')
const Bit = require('./Bit')
const AturanAssosiasi = require('./AturanAssosiasi')

class Apriori {
    static getSupport(db, supportData) {
        let itemUnik = db.getItemUnik();
        let itemSupportDua = new KoleksiItemset();
        let itemSupportTiga = new KoleksiItemset();
        let tandaItemIterasi = new KoleksiItemset();

        //Iterasi kedua setelah mendapatkan koleksi item
        for (var i = 0; i < itemUnik.length; i += 1) {
            itemSupportDua.push(Itemset.from([itemUnik[i]]));
        }

        //Iterasi terakhir!!!!!!
        let k = 2;
        while (itemSupportDua.length != 0) {
            // Hialngkan Item seng gak memenuhi syarat Support
            tandaItemIterasi.clear();
            for (var index in itemSupportDua) {
                let itemset = itemSupportDua[index];
                itemset.Support = db.cariAprioriSupport(itemset);
                if (itemset.Support >= supportData) {
                    tandaItemIterasi.push(itemset);
                    itemSupportTiga.push(itemset);
                }
            }

            // Set Ci for next iteration (find supersets of Li)
            itemSupportDua.clear();
            let subsets = Bit.findSubsets(tandaItemIterasi.getItemUnik(), k); // Get k-item subsets
            subsets.forEach(set => itemSupportDua.push(set));
            k++;
        }

        return itemSupportTiga;
    }

    static getConfidence(db, L, confidenceThreshold) {
        let allRules = [];
    
        for (var i in L) {
            let itemset = L[i];
            let subsets = Bit.findSubsets(itemset, 0); // Get all subsets
            for (var j in subsets) {
                let subset = subsets[j];
                //console.log(subsets);
                let confidence = (db.cariAprioriSupport(itemset) / db.cariAprioriSupport(subset)) * 100.0;
                if (confidence >= confidenceThreshold) {
                    let rule = new AturanAssosiasi();
                    subset.forEach(i => rule.X.push(i));
                    itemset.hapusItemset(subset).forEach(i => rule.Y.push(i));
                    rule.Support = db.cariAprioriSupport(itemset);
                    rule.Confidence = confidence;
                    //console.log(rule.Confidence);
                    if (rule.X.length > 0 && rule.Y.length > 0) {
                        allRules.push(rule);
                    }
                }
            }
        }

        return allRules;
    }
}

module.exports = Apriori