apriori算法Python实现
时间: 2023-11-05 12:04:22 浏览: 75
以下是 Apriori 算法的 Python 实现示例:
```python
def loadDataSet():
# 载入数据集
return [[1,3,4], [2,3,5], [1,2,3,5], [2,5]]
def createC1(dataSet):
# 创建候选项集C1
C1 = []
for transaction in dataSet:
for item in transaction:
if not [item] in C1:
C1.append([item])
C1.sort()
return list(map(frozenset, C1))
def scanD(D, Ck, minSupport):
# 计算支持度
ssCnt = {}
for tid in D:
for can in Ck:
if can.issubset(tid):
if not can in ssCnt:
ssCnt[can] = 1
else:
ssCnt[can] += 1
numItems = float(len(D))
retList = []
supportData = {}
for key in ssCnt:
support = ssCnt[key] / numItems
if support >= minSupport:
retList.append(key)
supportData[key] = support
return retList, supportData
def aprioriGen(Lk, k):
# 创建候选项集Ck
retList = []
lenLk = len(Lk)
for i in range(lenLk):
for j in range(i+1, lenLk):
L1 = list(Lk[i])[:k-2]
L2 = list(Lk[j])[:k-2]
L1.sort()
L2.sort()
if L1 == L2:
retList.append(Lk[i] | Lk[j])
return retList
def apriori(dataSet, minSupport=0.5):
# Apriori算法
C1 = createC1(dataSet)
D = list(map(set, dataSet))
L1, supportData = scanD(D, C1, minSupport)
L = [L1]
k = 2
while len(L[k-2]) > 0:
Ck = aprioriGen(L[k-2], k)
Lk, supK = scanD(D, Ck, minSupport)
supportData.update(supK)
L.append(Lk)
k += 1
return L, supportData
```
以上代码实现了 Apriori 算法的核心功能,可以通过调用 apriori 函数传入数据集和最小支持度参数来得到频繁项集和支持度信息。
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