关联分析算法jupyter
时间: 2023-07-19 19:42:43 浏览: 56
关联分析算法是一种数据挖掘技术,用于发现数据集中的频繁项集和关联规则。Jupyter是一个交互式笔记本,可以用于编写和共享代码、文本、数据可视化和其他文档。
在Jupyter中,可以使用Python编写关联分析算法。其中,最常用的算法是Apriori算法,该算法通过扫描数据集多次,逐步生成频繁项集和关联规则。
以下是一个使用Python实现Apriori算法的示例代码:
```
from itertools import combinations
def get_frequent_itemsets(transactions, min_support):
itemsets = []
for i in range(1, len(transactions[0])+1):
for subset in combinations(transactions[0], i):
support = sum(1 for transaction in transactions if set(subset).issubset(transaction))
if support >= min_support:
itemsets.append((subset, support))
return itemsets
def get_candidate_itemsets(frequent_itemsets):
candidate_itemsets = []
for i in range(len(frequent_itemsets)):
for j in range(i+1, len(frequent_itemsets)):
if len(frequent_itemsets[i][0].intersection(frequent_itemsets[j][0])) == len(frequent_itemsets[i][0])-1:
candidate_itemsets.append((frequent_itemsets[i][0].union(frequent_itemsets[j][0]), 0))
return candidate_itemsets
def get_frequent_itemsets_apriori(transactions, min_support):
frequent_itemsets = []
candidate_itemsets = get_frequent_itemsets(transactions, min_support)
frequent_itemsets += candidate_itemsets
while candidate_itemsets:
candidate_itemsets = get_candidate_itemsets(frequent_itemsets)
for transaction in transactions:
for candidate in candidate_itemsets:
if set(candidate[0]).issubset(transaction):
candidate = (candidate[0], candidate[1]+1)
frequent_itemsets += [candidate for candidate in candidate_itemsets if candidate[1] >= min_support]
return frequent_itemsets
transactions = [['apple', 'banana', 'pear'], ['banana', 'pear'], ['apple', 'banana', 'pear', 'orange'], ['apple', 'orange']]
min_support = 2
frequent_itemsets = get_frequent_itemsets_apriori(transactions, min_support)
print(frequent_itemsets)
```
这段代码使用Apriori算法来找到数据集中的频繁项集,并输出它们及其支持度。