Apriori

Basics:

Apriori is an algorithm for frequent item set mining and Association Rule Learning over transactional databases. The Apriori algorithm is used to perform itemset mining. Itemset mining let us find frequent patterns in data like if a consumer buys milk, he also buys bread. This type of pattern is called association rules and is used in many application domains.

It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database. The frequent item sets determined by Apriori can be used to determine association rules which highlight general trends in the database.

Association Rule Learning (ARL) is about the concept - People Who Bought Also Bought...

Few Examples:

To define the ARL, from the given data, Apriori Algorithm is used.
Apriori Algorithm has three components - Support, Confidence and Lift.
Below are the calculations of three:

Here are algorithm steps:

Code: Apriori

# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

# Data Preprocessing
dataset = pd.read_csv('Market_Basket_Optimisation.csv', header = None)
transactions = []
for i in range(0, 7501):
    transactions.append([str(dataset.values[i,j]) for j in range(0, 20)])

# Training Apriori on the dataset
from apyori import apriori
rules = apriori(transactions, min_support = 0.003, min_confidence = 0.2, min_lift = 3, min_length = 2)

# Visualising the results
results = list(rules)

Hope this helps!!

Arun Manglick

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