python实现apriori算法的核心代码

Apriori算法是一种用于关联规则挖掘的经典算法，其核心思想是基于“频繁项集”的发现来生成候选集，并通过剪枝过程找出支持度满足阈值的规则。以下是Python实现Apriori算法的一个简化版核心代码：

from collections import defaultdict

def create_candidatetstransactions(transactions, min_support):
    frequent_itemsets = []
    for transaction in transactions:
        frequent_itemsets.extend(transaction)

    # 初始化单个项目的频繁项集
    singletons = set(item for itemset in frequent_itemsets if len(itemset) == 1)
    
    # 使用Apriori算法生成候选集
    while len(singletons) > 0:
        current_level = [frozenset({item}) for item in singletons]
        
        next_candidates = []  # 存储下一层候选项
        for k in current_level:
            for i in singletons:
                if not {i} & k:  # 如果不相交，则合并
                    candidate = k | frozenset({i})
                    support = calculate_support(candidate, transactions, min_support)
                    if support >= min_support:
                        next_candidates.append((candidate, support))
        
        # 更新频繁项集
        frequent_itemsets.extend(next_candidates)
        singletons = {item for candidate, _ in next_candidates for item in candidate if len(candidate) > 1}
        
    return frequent_itemsets, next_candidates[-1][0] if next_candidates else None


def calculate_support(candidate, transactions, min_support):
    count = sum(1 for transaction in transactions if candidate.issubset(transaction))
    return count / len(transactions) if count >= min_support else 0


# 使用示例
transactions = [('Milk', 'Bread', 'Butter'), ('Bread', 'Butter', 'Eggs'), ('Milk', 'Butter', 'Eggs')]
min_support = 0.5

frequent_itemsets, best_itemset = create_candidatetstransactions(transactions, min_support)
print("频繁项集:", frequent_itemsets)

在这个代码中，`create_candidatetstransactions`函数负责生成频繁项集和候选集，`calculate_support`计算了给定项目集的支持度。你可以根据实际需求调整这个代码。