apriori算法_原理 + 代码|手把手教你用Python实现智能推荐算法
时间: 2024-02-01 09:04:24 浏览: 32
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Apriori算法是一种常用的关联规则挖掘算法,用于发现数据集中的频繁项集。其原理是通过迭代的方式不断削减候选项集的大小,找到所有满足最小支持度要求的频繁项集。
Apriori算法的步骤如下:
1. 初始化:找到所有单项集的支持度,并筛选出支持度大于等于最小支持度的频繁项集;
2. 迭代:根据频繁项集生成候选项集,并筛选出支持度大于等于最小支持度的频繁项集;
3. 终止条件:重复步骤2,直到无法再生成新的频繁项集。
下面是用Python实现Apriori算法的代码:
```python
def loadDataSet():
return [[1, 3, 4], [2, 3, 5], [1, 2, 3, 5], [2, 5]]
def createC1(dataSet):
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):
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.insert(0, key)
supportData[key] = support
return retList, supportData
def aprioriGen(Lk, k):
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):
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
```
以上就是用Python实现Apriori算法的代码,您可以根据自己的需求进行调整和修改。