在不使用apriori()函数的情况下，用R语言实现Apriori算法
在不使用apriori()函数的情况下，用R语言实现Apriori算法
提问于 2020-02-26 11:55:08
我正在尝试使用R语言实现Apriori，但重要的部分不是使用函数Apriori()，我应该从头开始构建它。所以我做了代码，但我的代码有一个问题，我不能解决它。我在代码中所做的是:我实现了Fk−1 × F1方法。但我的问题是，当我试图输入二进制文件时，它抛出了一个缺少值的错误！我将缺少的值替换为0，但它仍然抛出错误！我认为将原始购物篮转换为二进制形式的问题！
下面是我的代码：
multi_col = function(data_frame) {
multivec = data.frame(val = rep(1,nrow(data_frame)))
for(q in 1:ncol(data_frame)){
multivec = multivec*data_frame[q]
}
return(multivec)
}
item = c("onion","potato","milk","burger","beer")
t1 = c(1,1,0,1,0)
t2 = c(0,1,1,1,0)
t3 = c(1,1,1,0,0)
t4 = c(1,1,1,0,0)
t5 = c(1,1,1,1,0)
t6 =c(1,1,1,1,1)
data_mat= rbind(t1,t2,t3,t4,t5,t6)
data_mat
colnames(data_mat)=item
data_mat # this is the example data frame i used to develop the code
data_mat = as.data.frame(data_mat)
data_mat
min.sup.thresh = 2
max.item = ncol(data_mat)
max.item
for(k in 1:max.item){
if(ncol(data_mat)>1){
Candi = list()
Freq = list()
rm_col = numeric(0)
C_seq = combn(c(1:ncol(data_mat)),k)
for(i in 1:ncol(C_seq)){
Candi[[i]] = colnames(data_mat[C_seq[,i]])
if(sum(multi_col(data_mat[C_seq[,i]]))>=min.sup.thresh){
Freq[[i]] = colnames(data_mat[C_seq[,i]])
}else{
rm_col = c(rm_col,i)
}
}
data_mat=data_mat[(-rm_col)]
print(paste("number of generated candidate itemsets","in C",k,"is",length(Candi)))
print(Candi)
print("****************************")
print(paste("total number of frequent itemsets","in F",k,"is",length(Freq)))
print(Freq)
print("###################################################################################")
}
}你能给我一些关于如何做的建议吗？
回答 1
Stack Overflow用户
回答已采纳
发布于 2020-02-26 17:17:31
我尝试过使用您的代码，结果显示使用k==5时，行combn(c(1:ncol(data_mat)),k)出现错误，因为data_mat只有4列。我并没有真正理解你所有的代码，但我想这是因为你在循环中修改了data_mat。我设置了一个名为tmp_data_mat的新变量，这样就不会出现错误。
另一种选择是在for-loop之外修改data_mat
还要注意，milk中缺少一个值，它是通过在您使用的sum函数中添加na.rm = TRUE来工作的。
# I create data_mat on another way
data_mat <( data.frame(onion = c(1,0,rep(1,4)),
potato = rep(1,6),
milk = c(NA,rep(1,5)),
burger = c(1,1,0,0,1,1),
beer = c(rep(0,5),1)))
data_mat
min.sup.thresh = 2
max.item = ncol(data_mat)
max.item
for(k in 1:max.item){
if(ncol(data_mat)>1){
Candi = list()
Freq = list()
# modification here about rm_col, so it don't eat all your memory.
rm_col = seq(ncol(data_mat))
# here is the issue I think
C_seq = combn(c(1:ncol(data_mat)),k)
for(i in 1:ncol(C_seq)){
# don't think you need a function multi_col so I put it inside
data_frame <- data_mat[C_seq[,i]]
Candi[[i]] = colnames(data_frame)
multivec = data.frame(val = rep(1,nrow(data_frame)))
for(q in 1:ncol(data_frame)){
multivec = multivec*data_frame[q]
}
# the missing value error was because you missed the na.rm = TRUE in the sum function !
if(sum(multivec, na.rm = TRUE) >= min.sup.thresh){
Freq[[i]] = colnames(data_mat[C_seq[,i]])
}else{
# follow the modification of rm_col
rm_col = rm_col[-i]
}
}
# here is the BIG modification of your code it don't show error.
tmp_data_mat=data_mat[rm_col]
print(paste("number of generated candidate itemsets","in C",k,"is",length(Candi)))
print(Candi)
print("****************************")
print(paste("total number of frequent itemsets","in F",k,"is",length(Freq)))
print(Freq)
print("###################################################################################")
}
}页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/60406524
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