数据挖掘--购物车 推荐算法

 http://mayuxiang.sinaapp.com/?p=216

 关注另一种数据挖掘算法，这种算法主要用于频繁项集的发现。

熟悉网上购物的同学，一定都了解什么是“购物车” ，或者至少知道在超市中购物所推的购物车吧。亚马逊最早提出了一个算法，就是基于购物车的内容来为用户做出新的推荐。

一个简单的例子来看看什么是A-priori 算法

#!/usr/bin/python#coding:utf-8data = [['Cat','and','dog','bites'],['Yahoo','news','claims','cat','mated','with','a','dog','and','produced','viable','offspring'],['Cat','Killer','likely','is','a','big','dog'],['Professional','free','advice','on','dog','puppy','training'],['Cat','and','kitten','training','behavior'],['Dog','&','Cat','providers','dog','training','in','Engene','Oregon'],['Dog','and','cat','is','slang','term','used','by','police','officers','for','a','male-female','relationship'],['Shop','for','your','show','dog','grooming','and','pet','suppliers']]#wash the data and keep words unique in each rowdef delDuplication(data):pass  #ignore the implementing process here#ignore those words which rarely appeared#Threhold: set the smallest appear timesdef supportThreshold(res,threhold=1):res2={item:res[item] for item in res if len(res[item]) >= threhold}return res2  def statusCount():res = {} #define a dictionary to restore each word and its positiondelDuplication(data) #delete duplication words in data list        for i in range(0,len(data)):for word in data[i]:if res.has_key(word) == False:res[word]=[]res[word].append(i)res = supportThreshold(res,3)print "一维关联：",resreturn resdef comparePos(posX,posY):        # compare the two position list to find out# if the two word appear in same sentencespos =[]for p1 in posX:for p2 in posY:if p1 == p2:pos.append(p1)breakelif p2 > p1:breakreturn posdef apriori(res):  # apriori aglrithom, form a {res,res} conjunction pairhres={}for itemX in res:for itemY in res:  #compare with all elements behind itif itemX!= itemY and not hres.has_key((itemX,itemY)) and not hres.has_key((itemY,itemX)):pos = comparePos(res[itemX],res[itemY])pair = (itemX,itemY)hres[pair] = poshres = supportThreshold(hres,3)return hresif __name__ == "__main__":oneDe =statusCount()twoDe =apriori(oneDe)print "二维关联：",twoDefourDe=apriori(twoDe)print "四维关联:",fourDe