apfiof算法的python案例
时间: 2023-12-20 18:01:49 浏览: 75
apriori算法是一种用于在大规模数据集中发现频繁项集的数据挖掘算法,通常用于市场篮子分析和协同过滤。
下面是一个使用Python实现apriori算法的简单案例:
```python
from itertools import chain, combinations
from collections import defaultdict
def generate_candidate_itemsets(itemset, length):
return set([i.union(j) for i in itemset for j in itemset if len(i.union(j)) == length])
def frequent_itemsets(transactions, min_support):
itemset = set()
transaction_list = list(transactions)
for transaction in transaction_list:
for item in transaction:
itemset.add(frozenset([item]))
num_transactions = len(transactions)
min_support_count = num_transactions * min_support
all_frequent_items = {}
current_itemset = itemset
k = 2
while current_itemset != set([]):
all_frequent_items[k-1] = current_itemset
current_itemset = generate_candidate_itemsets(current_itemset, k)
k += 1
frequent_items = {}
for t in transaction_list:
for item in current_itemset:
if item.issubset(t):
frequent_items[item] = frequent_items.get(item, 0) + 1
current_itemset = set([item for item in current_itemset if frequent_items.get(item, 0) >= min_support_count])
return all_frequent_items
# 示例数据
dataset = [['milk', 'bread', 'butter'],
['milk', 'bread', 'butter', 'jam'],
['milk', 'bread', 'butter', 'tea'],
['milk', 'bread', 'butter'],
['tea', 'jam', 'bread']]
result = frequent_itemsets(dataset, 0.5)
print(result)
```
在这个简单的例子中,我们使用了一个包含商品购买信息的数据集。我们通过apriori算法找出了频繁项集,即经常一起购买的商品组合。这个案例展示了apriori算法在Python中的基本实现。
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