关联规则apriori算法实现一段代码

下面是一个简单的 Python 实现 Apriori 算法的示例代码：

def load_dataset():
    # 构建数据集
    dataset = [[1, 2, 3], [1, 3, 4], [2, 3, 5], [1, 2, 3, 4], [1, 2, 3, 5],
               [1, 2, 3, 6], [1, 2, 4, 5], [1, 2, 4, 6], [1, 3, 4, 5], [1, 3, 4, 6]]
    return dataset

def create_C1(dataset):
    # 生成候选1-项集
    C1 = []
    for transaction in dataset:
        for item in transaction:
            if not [item] in C1:
                C1.append([item])
    C1.sort()
    return list(map(frozenset, C1))

def scan_D(D, Ck, min_support):
    # 计算支持度
    ss_cnt = {}
    for tid in D:
        for can in Ck:
            if can.issubset(tid):
                if not can in ss_cnt:
                    ss_cnt[can] = 1
                else:
                    ss_cnt[can] += 1
    num_items = float(len(D))
    ret_list = []
    support_data = {}
    for key in ss_cnt:
        support = ss_cnt[key] / num_items
        if support >= min_support:
            ret_list.insert(0, key)
        support_data[key] = support
    return ret_list, support_data

def aprioriGen(Lk, k):
    # 连接步
    ret_list = []
    len_Lk = len(Lk)
    for i in range(len_Lk):
        for j in range(i + 1, len_Lk):
            L1 = list(Lk[i])[:k - 2]
            L2 = list(Lk[j])[:k - 2]
            L1.sort()
            L2.sort()
            if L1 == L2:
                ret_list.append(Lk[i] | Lk[j])
    return ret_list

def apriori(dataset, min_support=0.5):
    # Apriori 算法主函数
    C1 = create_C1(dataset)
    D = list(map(set, dataset))
    L1, support_data = scan_D(D, C1, min_support)
    L = [L1]
    k = 2
    while len(L[k - 2]) > 0:
        Ck = aprioriGen(L[k - 2], k)
        Lk, support_k = scan_D(D, Ck, min_support)
        support_data.update(support_k)
        L.append(Lk)
        k += 1
    return L, support_data

这段代码实现了 Apriori 算法的主要步骤，包括生成候选1-项集、计算支持度、连接步和剪枝步等。具体来说，`load_dataset` 函数用于构建数据集，`create_C1` 函数用于生成候选1-项集，`scan_D` 函数用于计算支持度，`aprioriGen` 函数用于进行连接步，`apriori` 函数是 Apriori 算法的主函数，用于迭代生成频繁项集。最后，`apriori` 函数返回频繁项集列表和支持度字典。