如何在python中组合链表中的字符串元素来实现Apriori算法？

# 生成所有可能的项集
def generate_itemsets(items, k):
    itemsets = []
    if k == 1:
        return [[item] for item in items]
    else:
        for i in range(len(items)):
            item = items[i]
            remaining_items = items[i+1:]
            for itemset in generate_itemsets(remaining_items, k-1):
                itemsets.append([item] + itemset)
    return itemsets

# 计算项集的支持度
def calculate_support(itemset, dataset):
    count = 0
    for transaction in dataset:
        if set(itemset).issubset(set(transaction)):
            count += 1
    return count

# 筛选频繁项集
def filter_frequent_itemsets(itemsets, dataset, min_support):
    frequent_itemsets = []
    for itemset in itemsets:
        support = calculate_support(itemset, dataset)
        if support >= min_support:
            frequent_itemsets.append(itemset)
    return frequent_itemsets

# 生成关联规则
def generate_association_rules(frequent_itemsets, dataset, min_confidence):
    association_rules = {}
    for itemset in frequent_itemsets:
        for i in range(1, len(itemset)):
            for antecedent in generate_itemsets(itemset, i):
                consequent = list(set(itemset) - set(antecedent))
                confidence = calculate_support(itemset, dataset) / calculate_support(antecedent, dataset)
                if confidence >= min_confidence:
                    association_rules[tuple(antecedent)] = (tuple(consequent), confidence)
    return association_rules

# 示例数据集
dataset = [
    ['A', 'B', 'C', 'D'],
    ['A', 'C', 'D'],
    ['A', 'B', 'C'],
    ['B', 'D'],
    ['A', 'B', 'C', 'D', 'E'],
]

# 设置参数
min_support = 2
min_confidence = 0.5

# 生成频繁项集
itemsets = []
for k in range(1, len(dataset[0])+1):
    itemsets.extend(generate_itemsets(list(set([item for transaction in dataset for item in transaction])), k))
frequent_itemsets = filter_frequent_itemsets(itemsets, dataset, min_support)

# 生成关联规则
association_rules = generate_association_rules(frequent_itemsets, dataset, min_confidence)

# 打印结果
print("频繁项集：")
for itemset in frequent_itemsets:
    print(itemset)
print("关联规则：")
for antecedent, (consequent, confidence) in association_rules.items():
    print("{} => {}: {}".format(antecedent, consequent, confidence))