R 协会 - 为什么 LHS 返回空白？答案

【问题标题】：R Association - Why is LHS returning blank?R 协会 - 为什么 LHS 返回空白？
【发布时间】：2020-05-11 02:14:32
【问题描述】：

我对 R 很陌生，正在尝试为一组事务运行 apriori 函数。我在检查遇到问题的规则时发现，LHS 是空的。我究竟做错了什么？

下面是我使用的代码。我还附上了两张我使用过的不同文件格式的图片。数据格式1、数据格式2、数据格式3。下面的代码使用的是格式3。

> dianacsv <- read.csv("diana.csv")

> dianatrans <- read.transactions(file="diana.csv", format = c("basket"), header = TRUE, sep = ",")

> summary(dianatrans)
transactions as itemMatrix in sparse format with
 114091 rows (elements/itemsets/transactions) and
 114149 columns (items) and a density of 3.32023e-05 

most frequent items:
   CS12    PS12    BU12    GB12    CC12 (Other) 
  23819   18268   16166   15544   14452  344157 

element (itemset/transaction) length distribution:
sizes
    3     4     5     6     7     8     9    10    11    12    13    14    15    16    17 
65024 26446 13237  5185  2243   873   435   250   133    83    65    47    26     8    17 
   18    19    20    21    22    23    24 
    3     3     6     3     1     2     1 

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   3.00    3.00    3.00    3.79    4.00   24.00 

includes extended item information - examples:
  labels
1      1
2     10
3    100

> dianarules <- apriori(dianatrans, parameter = list(supp = 0.01, conf = 0.01, target = "rules"))
Apriori

Parameter specification:
 confidence minval smax arem  aval originalSupport maxtime support minlen maxlen target
       0.01    0.1    1 none FALSE            TRUE       5    0.01      1     10  rules
   ext
 FALSE

Algorithmic control:
 filter tree heap memopt load sort verbose
    0.1 TRUE TRUE  FALSE TRUE    2    TRUE

Absolute minimum support count: 1140 

set item appearances ...[0 item(s)] done [0.00s].
set transactions ...[114149 item(s), 114091 transaction(s)] done [0.23s].
sorting and recoding items ... [38 item(s)] done [0.01s].
creating transaction tree ... done [0.04s].
checking subsets of size 1 2 3 done [0.00s].
writing ... [237 rule(s)] done [0.00s].
creating S4 object  ... done [0.02s].

> inspect(dianarules[1:5])
    lhs    rhs     support    confidence lift count
[1] {}  => {BBK16} 0.01076334 0.01076334 1    1228 
[2] {}  => {CS4}   0.01036892 0.01036892 1    1183 
[3] {}  => {BU4}   0.01196413 0.01196413 1    1365 
[4] {}  => {CHK16} 0.01362071 0.01362071 1    1554 
[5] {}  => {PJK16} 0.01575059 0.01575059 1    1797

数据格式1

数据格式2

数据格式3

【问题讨论】：

嗯...您的规则子集有一个空的 LHS。我猜如果你检查更多规则，你会用非空 LHS 来处理一些规则。

标签： r associations data-mining apriori market-basket-analysis

【解决方案1】：

使用 apriori 时必须设置 minlen：

library(arules)
M = matrix(sample(0:1,100,replace=TRUE),ncol=10)
rownames(M) = paste0("tx",1:10)
colnames(M) = paste0("item",1:10)

M.rules = apriori(as(M,"transactions"), 
parameter = list(supp = 0.01, conf = 0.01, target = "rules"))

inspect(M.rules[1:5])
    lhs    rhs     support confidence lift count
[1] {}  => {item1} 0.2     0.2        1    2    
[2] {}  => {item2} 0.4     0.4        1    4    
[3] {}  => {item4} 0.4     0.4        1    4    
[4] {}  => {item9} 0.5     0.5        1    5    
[5] {}  => {item7} 0.6     0.6        1    6   

M.rules = apriori(as(M,"transactions"), 
parameter = list(supp = 0.01, conf = 0.01, 
target = "rules",minlen=2))

inspect(M.rules[1:5])
    lhs        rhs     support confidence lift      count
[1] {item1} => {item4} 0.1     0.50       1.2500000 1    
[2] {item4} => {item1} 0.1     0.25       1.2500000 1    
[3] {item1} => {item9} 0.1     0.50       1.0000000 1    
[4] {item9} => {item1} 0.1     0.20       1.0000000 1    
[5] {item1} => {item7} 0.1     0.50       0.8333333 1

【讨论】：