'''
Below is webmining poc project based on site urls visited by each user in one day.
Algorithm below finds out frequent set visited urls with;
- Minimum Support Factor (configurable)
- Minimum Confident (configurable)
Result of below run with minSupport=0.6 and minConf=0.6 is;
[(frozenset([6]), frozenset([4]), 0.75), (frozenset([4]), frozenset([6]), 1.0)]
than means, whenever a user visited url 6, s/he visted url 4 and confidence on this finding is 0.75
whereas whenever a user visited url 4 s/he visited url 6 with confidence 1.0.
Download and run as iPython Notebook
'''
from numpy import *
# Currently this data is taken as a few entries in msnbc990928.seq for WebMining project demostration
#
def loadDataSet():
return [[6, 9, 4, 4, 4, 10, 3, 10, 5, 10, 4, 4, 4 ], [6, 9, 9, 9, 9, 7, 9 ], [1, 4, 7, 1, 10, 10, 1, 2, 2, 1, 1, 6, 1], [1,2,4, 5,6]]
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)#use frozen set so we
#can use it as a key in a dict
def scanD(D, Ck, minSupport):
ssCnt = {}
for tid in D:
for can in Ck:
if can.issubset(tid):
if not ssCnt.has_key(can): 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): #creates 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()
#print "L1:",L1
#print "L2:",L2
#compare the first items to avoid duplicate
if L1==L2: #if first k-2 elements are equal,namely,besides the last item,all the items of the two sets are the same!
retList.append(Lk[i] | Lk[j]) #set union
return retList
def apriori(dataSet, minSupport = 0.8):
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)#scan DB to get Lk
supportData.update(supK)
L.append(Lk)
k += 1
return L, supportData
def generateRules(L, supportData, minConf=0.7): #supportData is a dict coming from scanD
bigRuleList = []
for i in range(1, len(L)):#only get the sets with two or more items
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
def calcConf(freqSet, H, supportData, brl, minConf=0.7):
prunedH = [] #create new list to return
for conseq in H:
conf = supportData[freqSet]/supportData[freqSet-conseq] #calc confidence
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):
print "freqSet:",freqSet
Hmp1=calcConf(freqSet, H, supportData, brl, minConf)
m = len(Hmp1[0])
#print "m:",m,"Hmp1 now:",Hmp1
if (len(freqSet) > (m + 1)): #try further merging
Hmp1 = aprioriGen(Hmp1, m+1)#create Hm+1 new candidates
#print 'Hmp1:',Hmp1
Hmp1 = calcConf(freqSet, Hmp1, supportData, brl, minConf)
#print 'Hmp1 after calculate:',Hmp1
if (len(Hmp1) > 1): #need at least two sets to merge
rulesFromConseq(freqSet, Hmp1, supportData, brl, minConf)
'''
dataset=loadDataSet()
C1=createC1(dataset)
retList,supportData=scanD(dataset,C1,0.5)
print 'C1:',C1
print 'retList:',retList
print 'supportData:',supportData
'''
dataSet=loadDataSet()
#input together with data set is minimum Support factor
L,supportData=apriori(dataSet,0.6)
#input together with the supportData is minimum Confidence
brl=generateRules(L, supportData,0.6)
print 'Following are frequent visited websites with minimum Support factor: 0.7 and minimum Confidence: 0.7\n'
print brlFollowing are frequent visited websites with minimum Support factor: 0.7 and minimum Confidence: 0.7
[(frozenset([6]), frozenset([4]), 0.75), (frozenset([4]), frozenset([6]), 1.0)]