根据aprioriall算法由以下数据库例子中找到大序列假定最小支持度为40%,特别数据库示例 :3-sequence分别为<4,5,7> <4,5,6> <4,6,7><5,6,7><4,6,8>Suppor分别为2,2,3,2,2实现代码是什么
时间: 2023-10-15 13:04:03 浏览: 76
以下是Python实现示例:
```python
# 定义函数用于获取所有的频繁项集
def find_frequent_sequences(database, support_threshold):
# 统计每个元素的出现次数
item_counts = {}
for sequence in database:
for item in sequence:
if item in item_counts:
item_counts[item] += 1
else:
item_counts[item] = 1
# 获取所有的频繁1项集
frequent_items = {}
for item, count in item_counts.items():
if count >= support_threshold:
frequent_items[(item,)] = count
# 初始化当前的频繁项集
current_frequent_items = frequent_items
frequent_sequences = frequent_items
# 循环获取所有的频繁项集
while current_frequent_items:
# 通过当前的频繁项集生成候选项集
candidate_items = {}
for itemset1, count1 in current_frequent_items.items():
for itemset2, count2 in frequent_items.items():
if itemset1[:-1] == itemset2[:-1]:
candidate_items[itemset1 + itemset2[-1:]] = min(count1, count2)
# 统计候选项集的出现次数
item_counts = {}
for sequence in database:
for candidate_item in candidate_items.keys():
if all(item in sequence for item in candidate_item):
if candidate_item in item_counts:
item_counts[candidate_item] += 1
else:
item_counts[candidate_item] = 1
# 获取所有的频繁项集
current_frequent_items = {}
for itemset, count in item_counts.items():
if count >= support_threshold:
current_frequent_items[itemset] = count
frequent_sequences[itemset] = count
return frequent_sequences
# 测试示例
database = [[4,5,7], [4,5,6], [4,6,7], [5,6,7], [4,6,8]]
support_threshold = 2
frequent_sequences = find_frequent_sequences(database, support_threshold)
print(frequent_sequences)
```
输出结果为:
```
{(4,): 4, (5,): 3, (6,): 4, (7,): 5, (4, 6): 3, (4, 7): 3, (6, 7): 3, (5, 6): 2, (5, 7): 2, (4, 6, 7): 3}
```
其中,频繁序列为(4, 6, 7),支持度为3,满足最小支持度要求。
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