python实现PCY算法
时间: 2023-12-01 10:43:18 浏览: 111
基于Python的PCY算法实现步骤如下:
1.首先,我们需要读取数据集并将其转换为事务列表。
2.接下来,我们需要计算每个项的支持度,并使用PCY算法计算每个项对的支持度。
3.然后,我们需要根据最小支持度阈值过滤掉不频繁的项对。
4.最后,我们需要使用频繁项对生成关联规则。
下面是一个简单的Python实现:
```python
def load_dataset():
# 读取数据集并将其转换为事务列表
dataset = [['apple', 'banana', 'orange'],
['banana', 'orange', 'pear'],
['orange', 'pear', 'apple'],
['pear', 'apple', 'banana']]
return dataset
def create_candidates(dataset, k):
# 生成候选项集
candidates = {}
for transaction in dataset:
for i in range(len(transaction)):
for j in range(i+1, len(transaction)):
itemset = frozenset([transaction[i], transaction[j]])
if itemset in candidates:
candidates[itemset] += 1
else:
candidates[itemset] = 1
return candidates
def filter_candidates(candidates, min_support):
# 根据最小支持度阈值过滤掉不频繁的项对
frequent_candidates = {}
for itemset in candidates:
if candidates[itemset] >= min_support:
frequent_candidates[itemset] = candidates[itemset]
return frequent_candidates
def generate_rules(frequent_candidates, min_confidence):
# 使用频繁项对生成关联规则
rules = []
for itemset in frequent_candidates:
if len(itemset) > 1:
for item in itemset:
antecedent = frozenset([item])
consequent = itemset - antecedent
if len(consequent) > 0:
confidence = frequent_candidates[itemset] / frequent_candidates[antecedent]
if confidence >= min_confidence:
rules.append((antecedent, consequent, confidence))
return rules
def pcy_algorithm(dataset, min_support, min_confidence):
# PCY算法实现
candidates = create_candidates(dataset, 2)
frequent_candidates = filter_candidates(candidates, min_support)
rules = generate_rules(frequent_candidates, min_confidence)
return rules
# 测试
dataset = load_dataset()
rules = pcy_algorithm(dataset, 2, 0.5)
print(rules)
```
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