本文详细介绍了使用Python的apyori库实现Apriori算法的过程，包括数据预处理、训练Apriori模型及结果可视化。通过读取Market_Basket_Optimisation数据集，将交易数据转换为适合Apriori算法的格式，设定最小支持度、置信度、提升度和最小项集长度等参数，最终展示Apriori规则的可视化结果。
# 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)
myResults = [list(x) for x in results]
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