Question

我已经下载了last.fm数据集，其中包含用户和他们听过的歌曲。我试图建立联想，听一个作者是否意味着听另一个作者。

数据集来自last.fm，有359349位用户和292542位艺术家。我首先使用dMcast（）从用户和艺术家列构建布尔矩阵。然后转换为

这是我的代码：

library(data.table)
library(Matrix)
library(Matrix.utils)
library(magrittr)
library(arules)

col_names <- c(
  "user_id",
  "id2",
  "artist",
  "song"
)

last.fm <- fread("R/upworkR/Rex - association rules/lastfm-dataset-360K/usersha1-artmbid-artname-plays.tsv", 
            sep = '\t',
            col.names = col_names,encoding = 'UTF-8')


last.fm$id1 <- as.factor(last.fm$id1)
(last.fm)

last.fm.matrix <- dMcast(last.fm, formula = user_id ~ artist)
last.fm.ngcM <- as(last.fm.matrix, "ngCMatrix") %>% as("transactions")
music.rules <- apriori(last.fm.ngcM, parameter = list(supp=0.0009, conf=0.3))

我尝试了各种支持和信心，但是它要么不生成规则，要么出现内存问题。我也改变了maxlen和maxtime，但还没有运气。输出如下：

Parameter specification:  confidence minval smax arem  aval originalSupport maxtime support minlen maxlen target   ext
        0.3    0.1    1 none FALSE            TRUE       5   9e-04      1     10  rules FALSE

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

Absolute minimum support count: 263 

set item appearances ...[0 item(s)] done [0.00s]. set transactions ...[359349 item(s), 292542 transaction(s)] done [26.21s]. sorting and recoding items ... [0 item(s)] done [0.14s]. creating transaction tree ... done [0.02s]. checking subsets of size 1 done [0.00s]. writing ... [0 rule(s)] done [0.00s]. creating S4 object  ... done [0.10s].

Apriori不生成任何规则

0 个答案: