python实现Apriori算法
根据我们上个博客的例子
def load_dataset():# 载入数据集的函数dataset = [['A', '', 'D'],['B', 'C', 'E'],['A', 'B', 'C', 'E'],['B', 'E']]return datasetdef create_candidates(dataset):# 生成1项集的候选集函数candidates = []for transaction in dataset:for item in transaction:if [item] not in candidates:candidates.append([item])candidates.sort()return list(map(frozenset, candidates))def scan_dataset(dataset, candidates, min_support):# 扫描数据集，计算候选集的支持度item_count = {} # 记录候选集出现的次数for transaction in dataset:for candidate in candidates:if candidate.issubset(transaction):item_count[candidate] = item_count.get(candidate, 0) + 1num_transactions = len(dataset)frequent_set = [] # 存储频繁项集support_data = {} # 存储支持度数据for item in item_count:support = item_count[item] / num_transactionsif support >= min_support:frequent_set.append(item)support_data[item] = supportreturn frequent_set, support_datadef generate_next_candidates(prev_frequent_set, k):# 生成下一轮的候选集函数next_candidates = [] # 存储下一轮的候选集num_frequent_set = len(prev_frequent_set)for i in range(num_frequent_set):for j in range(i + 1, num_frequent_set):item1 = list(prev_frequent_set[i])[:k - 2]item2 = list(prev_frequent_set[j])[:k - 2]item1.sort()item2.sort()if item1 == item2:next_candidate = prev_frequent_set[i] | prev_frequent_set[j]next_candidates.append(next_candidate)return next_candidatesdef apriori(dataset, min_support=0.5):# Apriori 算法主函数candidates = create_candidates(dataset)dataset = list(map(set, dataset))frequent_set1, support_data = scan_dataset(dataset, candidates, min_support)frequent_sets = [frequent_set1]k = 2while len(frequent_sets[k - 2]) > 0:candidates = generate_next_candidates(frequent_sets[k - 2], k)frequent_set, support_data_k = scan_dataset(dataset, candidates, min_support)support_data.update(support_data_k)frequent_sets.append(frequent_set)k += 1return frequent_sets, support_data# 示例用法
dataset = load_dataset()
frequent_sets, support_data = apriori(dataset, min_support=0.5)print("频繁项集:")
for i, itemset in enumerate(frequent_sets):if itemset:print(f"第 {i + 1} 轮: {itemset}")print("\n支持度数据:")
for item, support in support_data.items():print(f"{item}: {support}")结果
频繁项集:
第 1 轮: [frozenset({‘A’}), frozenset({‘B’}), frozenset({‘C’}), frozenset({‘E’})]
第 2 轮: [frozenset({‘C’, ‘B’}), frozenset({‘E’, ‘B’}), frozenset({‘C’, ‘E’})]
第 3 轮: [frozenset({‘C’, ‘E’, ‘B’})]
支持度数据:
frozenset({‘A’}): 0.5
frozenset({‘D’}): 0.25
frozenset({‘B’}): 0.75
frozenset({‘C’}): 0.5
frozenset({‘E’}): 0.75
frozenset({‘C’, ‘B’}): 0.5
frozenset({‘E’, ‘B’}): 0.75
frozenset({‘C’, ‘E’}): 0.5
frozenset({‘A’, ‘B’}): 0.25
frozenset({‘C’, ‘A’}): 0.25
frozenset({‘A’, ‘E’}): 0.25
frozenset({‘C’, ‘E’, ‘B’}): 0.5
