编写程序完成下列算法: 1、apriori算法 输入: 数据集d;最小支持数minsup_count;

以下是 Python 代码实现 Apriori 算法：

from collections import defaultdict

def load_data():
    data = [['A', 'B', 'C', 'E'], ['A', 'B', 'C', 'D'], ['A', 'B', 'C'], ['A', 'B'], ['B', 'C', 'E']]
    return data

def create_C1(data):
    C1 = set()
    for transaction in data:
        for item in transaction:
            itemset = frozenset([item])
            C1.add(itemset)
    return C1

def support_count(data, candidate, minsup_count):
    count = 0
    for transaction in data:
        if candidate.issubset(transaction):
            count += 1
    return count >= minsup_count

def generate_candidates(Lk, k):
    candidates = set()
    for i in Lk:
        for j in Lk:
            union = i.union(j)
            if len(union) == k:
                candidates.add(union)
    return candidates

def apriori(data, minsup_count):
    C1 = create_C1(data)
    L1 = {}
    for candidate in C1:
        if support_count(data, candidate, minsup_count):
            L1[candidate] = support_count(data, candidate, minsup_count)
    L = [L1]
    k = 2
    while len(L[k-2]) > 0:
        Ck = generate_candidates(L[k-2].keys(), k)
        Lk = defaultdict(int)
        for candidate in Ck:
            if support_count(data, candidate, minsup_count):
                Lk[candidate] = support_count(data, candidate, minsup_count)
        L.append(Lk)
        k += 1
    return L

if __name__ == '__main__':
    data = load_data()
    minsup_count = 3
    L = apriori(data, minsup_count)
    print(L)

在这个例子中，数据集为一个列表，每个元素表示一个事务，每个事务由若干个项组成。`create_C1()` 函数用于生成初始候选项集，`support_count()` 函数用于计算项集的支持度，`generate_candidates()` 函数用于生成候选项集，`apriori()` 函数实现了 Apriori 算法，返回项集的列表。在这个例子中，最小支持数为3，即项集出现的次数至少为3次才会被保留。