Apriori算法的python实现
时间: 2023-11-10 21:15:31 浏览: 32
Apriori算法是一种关联规则挖掘算法,用于发现数据集中的频繁项集。下面是Apriori算法的Python实现:
1. 定义函数loadDataSet()用于加载数据集。
```python
def loadDataSet():
return [[1,3,4],[2,3,5],[1,2,3,5],[2,5]]
```
2. 定义函数createC1()用于生成候选项集C1。
```python
def createC1(dataSet):
C1 = []
for transaction in dataSet:
for item in transaction:
if not [item] in C1:
C1.append([item])
C1.sort()
return map(frozenset, C1)
```
3. 定义函数scanD()用于扫描数据集D,生成满足最小支持度的频繁项集。
```python
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
```
4. 定义函数aprioriGen()用于生成候选项集Ck。
```python
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
```
5. 定义函数apriori()用于生成所有频繁项集。
```python
def apriori(dataSet, minSupport=0.5):
C1 = createC1(dataSet)
D = 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
```
6. 定义函数generateRules()用于生成关联规则。
```python
def generateRules(L, supportData, minConf=0.7):
bigRuleList = []
for i in range(1, len(L)):
for freqSet in L[i]:
H1 = [frozenset([item]) for item in freqSet]
if (i > 1):
rulesFromConseq(freqSet, H1, supportData, bigRuleList, minConf)
else:
calcConf(freqSet, H1, supportData, bigRuleList, minConf)
return bigRuleList
```
7. 定义函数calcConf()和rulesFromConseq()用于计算置信度和生成关联规则。
```python
def calcConf(freqSet, H, supportData, brl, minConf=0.7):
prunedH = []
for conseq in H:
conf = supportData[freqSet]/supportData[freqSet-conseq]
if conf >= minConf:
print(freqSet-conseq,'-->',conseq,'conf:',conf)
brl.append((freqSet-conseq, conseq, conf))
prunedH.append(conseq)
return prunedH
def rulesFromConseq(freqSet, H, supportData, brl, minConf=0.7):
m = len(H[0])
if (len(freqSet) > (m + 1)):
Hmp1 = aprioriGen(H, m+1)
Hmp1 = calcConf(freqSet, Hmp1, supportData, brl, minConf)
if (len(Hmp1) > 1):
rulesFromConseq(freqSet, Hmp1, supportData, brl, minConf)
```
8. 最后,调用apriori()和generateRules()函数进行频繁项集挖掘和关联规则挖掘。
```python
dataSet = loadDataSet()
L, supportData = apriori(dataSet, 0.5)
rules = generateRules(L, supportData, 0.7)
```