关联算法有几个重要的概念：
下面以官方教程为例

[['Apple', 'Beer', 'Rice', 'Chicken'],
           ['Apple', 'Beer', 'Rice'],
           ['Apple', 'Beer'],
           ['Apple', 'Bananas'],
           ['Milk', 'Beer', 'Rice', 'Chicken'],
           ['Milk', 'Beer', 'Rice'],
           ['Milk', 'Beer'],
           ['Apple', 'Bananas']]

支持度
例子

置信度

from mlxtend.preprocessing import TransactionEncoder
info = [['Apple', 'Beer', 'Rice', 'Chicken'],
           ['Apple', 'Beer', 'Rice'],
           ['Apple', 'Beer'],
           ['Apple', 'Bananas'],
           ['Milk', 'Beer', 'Rice', 'Chicken'],
           ['Milk', 'Beer', 'Rice'],
           ['Milk', 'Beer'],
           ['Apple', 'Bananas']]
#print(filelist)
TE = TransactionEncoder()
datas = TE.fit_transform(info)

import pandas as pd
df = pd.DataFrame(datas, columns=TE.columns_)
print(df)
from mlxtend.frequent_patterns import apriori
item = apriori(df, min_support=0.4, use_colnames=True)
item[item['itemsets'].apply(lambda x: len(x))>=2]
print(item)

from mlxtend.frequent_patterns import  association_rules
rules = association_rules(item,min_threshold=0.8)
print(rules)
for i, j in rules.iterrows():
    X = j['antecedents']
    Y = j['consequents']
    x = ','.join(item for item in X )
    y = ','.join(item for item in Y)
    print(x + '->'+y)