A small example database for weighted association rule mining provided as an object of class transactions.
Format
Object of class transactions with 6 transactions and 8 items. Weights are stored as transaction information.
Source
K. Sun and F. Bai (2008). Mining Weighted Association Rules without Preassigned Weights. IEEE Transactions on Knowledge and Data Engineering, 4 (30), 489–495.
Details
The data set contains the example database described in the paper by K. Sun
and F.Bai for illustration of the concepts of weighted association rule
mining. weight stored as transaction information denotes the
transaction weights obtained using the HITS algorithm.
Examples
data(SunBai)
summary(SunBai)
#> transactions as itemMatrix in sparse format with
#> 6 rows (elements/itemsets/transactions) and
#> 8 columns (items) and a density of 0.375
#>
#> most frequent items:
#> A C G B F (Other)
#> 4 3 3 2 2 4
#>
#> element (itemset/transaction) length distribution:
#> sizes
#> 1 2 3 4 5
#> 1 1 2 1 1
#>
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 1.00 2.25 3.00 3.00 3.75 5.00
#>
#> includes extended item information - examples:
#> labels
#> 1 A
#> 2 B
#> 3 C
#>
#> includes extended transaction information - examples:
#> transactionID weight
#> 1 100 0.5176528
#> 2 200 0.4362571
#> 3 300 0.2321374
inspect(SunBai)
#> items transactionID weight
#> [1] {A, B, C, D, E} 100 0.5176528
#> [2] {C, F, G} 200 0.4362571
#> [3] {A, B} 300 0.2321374
#> [4] {A} 400 0.1476262
#> [5] {C, F, G, H} 500 0.5440458
#> [6] {A, G, H} 600 0.4123691
transactionInfo(SunBai)
#> transactionID weight
#> 1 100 0.5176528
#> 2 200 0.4362571
#> 3 300 0.2321374
#> 4 400 0.1476262
#> 5 500 0.5440458
#> 6 600 0.4123691