I'm using R code in Knime, a platform for data analytics. I take a matrix from a block which reads a table from an xls file. This matrix is called "my_matrix". Then I'd like to applicate the apriori algorithm to three columns of this matrix, but I get the above error. Any ideas? Please note that "sum(is.na(my_matrix))" has output [1] 0
require(arules)
#require(arulesViz)
#some code to retreive the my_matrix
#my_matrix
my_rows= nrow(my_matrix);
my_cols= ncol(my_matrix);
matrix_temp = my_matrix[,4:5];
matrix_temp = array(c(matrix_temp, my_matrix[,20]), dim=c(my_rows,3))
#matrix_temp
my_matrix = matrix_temp
sum(is.na(my_matrix)) #output: [1] 0
my_transactions = as(my_matrix, "transactions");
summary(my_transactions)
my_matrix is a table in which columns are this type: (String, Integer, Integer, String, String, Integer, Double, Integer, Double, Double, Integer, Double, Double, Integer, Double, Double, Integer, Double, Integer, String). There are 3300 rows in the table.
> my_matrix = as(knime.in, "matrix")
> dput(head(my_matrix, 5))
structure(c("KS", "OH", "NJ", "OH", "OK", "128", "107", "137",
" 84", " 75", "415", "415", "415", "408", "415", "noIP", "noIP",
"noIP", "IP", "IP", "Vmail", "Vmail", "noVmail", "noVmail", "noVmail",
"25", "26", " 0", " 0", " 0", "265.1", "161.6", "243.4", "299.4",
"166.7", "110", "123", "114", " 71", "113", "45.07", "27.47",
"41.38", "50.90", "28.34", "197.4", "195.5", "121.2", " 61.9",
"148.3", " 99", "103", "110", " 88", "122", "16.78", "16.62",
"10.30", " 5.26", "12.61", "244.7", "254.4", "162.6", "196.9",
"186.9", " 91", "103", "104", " 89", "121", "11.01", "11.45",
" 7.32", " 8.86", " 8.41", "10.0", "13.7", "12.2", " 6.6", "10.1",
" 3", " 3", " 5", " 7", " 3", "2.70", "3.70", "3.29", "1.78",
"2.73", "1", "1", "0", "2", "3", "notChurning", "notChurning",
"notChurning", "notChurning", "notChurning"), .Dim = c(5L, 20L
), .Dimnames = list(c("Row0", "Row1", "Row2", "Row3", "Row4"),
c("State", "Account length", "Area code", "International plan",
"Voice mail plan", "Number vmail messages", "Total day minutes",
"Total day calls", "Total day charge", "Total eve minutes",
"Total eve calls", "Total eve charge", "Total night minutes",
"Total night calls", "Total night charge", "Total intl minutes",
"Total intl calls", "Total intl charge", "Customer service calls",
"Churn")))
I have modified my code as below. I have no errors now, but the apriori algorithm founds no rules:
require(arules)
#require(arulesViz)
my_matrix= as(knime.in,"matrix");
my_rows= nrow(my_matrix);
my_cols= ncol(my_matrix);
my_matrix = append(my_matrix[,4:5], my_matrix[,20])
my_matrix <- array(my_matrix, dim=c(my_rows, 3))
head(my_matrix, 2)
my_cols = 3
my_dedup_matrix= matrix(nrow=my_rows, ncol=my_cols);
for (i in 1:my_rows) {
m = my_matrix[i,];
my_unique= unique(m);
for (j in 1:my_cols) {
my_dedup_matrix[i,j] = my_unique[j];
}
}
my_new_data= apply(my_dedup_matrix, 1, function(x) x[!is.na(x)]);
my_transactions = as(as.list(my_new_data), "transactions");
summary(my_transactions)
## Mine association rules.
rules = apriori(my_transactions, parameter = list(supp= 0.1, conf= 0.8, target = "rules"));
summary(rules); # no rules found
inspect(rules); # no rules found
#inspect(rules[1:10])
You need to convert my_matrix first into a list.
as(as.list(my_matrix),"transactions")
However, I get a different error.
as(as.list(matrix_temp),"transactions")
transactions in sparse format with
15 transactions (rows) and
5 items (columns)