apriori算法代码

当然，我可以帮你提供 Apriori 算法的示例代码。以下是一个基于 Python 的简单实现：

def apriori(transactions, min_support):
    itemsets = find_frequent_1_itemsets(transactions, min_support)
    all_frequent_itemsets = itemsets.copy()
    
    k = 2
    while itemsets:
        itemsets = generate_candidates(itemsets, k)
        frequent_itemsets = find_frequent_k_itemsets(itemsets, transactions, min_support)
        all_frequent_itemsets.update(frequent_itemsets)
        k += 1
    
    return all_frequent_itemsets

def find_frequent_1_itemsets(transactions, min_support):
    item_counts = {}
    for transaction in transactions:
        for item in transaction:
            if item in item_counts:
                item_counts[item] += 1
            else:
                item_counts[item] = 1
    
    num_transactions = len(transactions)
    frequent_itemsets = {}
    for item, count in item_counts.items():
        support = count / num_transactions
        if support >= min_support:
            frequent_itemsets[frozenset([item])] = support
    
    return frequent_itemsets

def generate_candidates(itemsets, k):
    candidates = set()
    for itemset1 in itemsets:
        for itemset2 in itemsets:
            union = itemset1.union(itemset2)
            if len(union) == k:
                candidates.add(union)
    
    return candidates

def find_frequent_k_itemsets(candidates, transactions, min_support):
    item_counts = {}
    for transaction in transactions:
        for candidate in candidates:
            if candidate.issubset(transaction):
                if candidate in item_counts:
                    item_counts[candidate] += 1
                else:
                    item_counts[candidate] = 1
    
    num_transactions = len(transactions)
    frequent_itemsets = {}
    for itemset, count in item_counts.items():
        support = count / num_transactions
        if support >= min_support:
            frequent_itemsets[itemset] = support
    
    return frequent_itemsets

使用示例：

transactions = [
    ['A', 'B', 'D'],
    ['B', 'C', 'E'],
    ['A', 'B', 'C', 'E'],
    ['B', 'E']
]

min_support = 0.5

frequent_itemsets = apriori(transactions, min_support)
print(frequent_itemsets)

这段代码实现了 Apriori 算法的关键部分，包括找到频繁 1 项集、生成候选项集、找到频繁 k 项集等步骤。你可以根据自己的数据和需求进行相应的调整和扩展。希望对你有所帮助！