人工智能和机器学习之关联规则学习算法：Sequence Mining：关联规则学习基础理论.docx

te = TransactionEncoder()

te_ary = te.fit(dataset).transform(dataset)

df = pd.DataFrame(te_ary, columns=te.columns_)

sequences = frequent_sequence_mining(df, min_support=0.4)

print(sequences)

from itertools import combinations

def apriori(transactions, min_support):

#

frequent_items = {}

for transaction in transactions:

for item in transaction:

frequent_items = {item: support for item, support in frequent_items.items() if support / total_

transactions >= min_support}

#

def generate_candidates(frequent_items):