Prepare Data for Associative Classification

Converts a data.frame into transactions suitable for classification based on association rules.

Usage

prepareTransactions(
  formula,
  data,
  disc.method = "mdlp",
  logical2factor = TRUE,
  match = NULL
)

Arguments

formula

the formula.

data

A data.frame containing the data.

disc.method

Discretization method used to discretize continuous variables if data is a data.frame (default: "mdlp"). See discretizeDF.supervised() for more supervised discretization methods.

logical2factor

logical; if data is a data.frame, should logical columns be recoded as factors with TRUE/FALSE levels to generate positive and negative items?

match

Typically NULL. Used internally only if data is already a set of transactions.

Value

An object of class arules::transactions from arules with an attribute called "disc_info" that contains information on the used discretization for each column.

Details

To convert a data.frame into items in a transaction dataset for classification, the following steps are performed:

1. All continuous features are discretized using class-based discretization (default is MDLP) and each range is represented as an item.

2. Factors are converted into items, one item for each level.

3. Each logical variable is converted into an item.

4. If the class variable is logical, a negative class item is added.

Steps 1-3 are skipped if data is already a arules::transactions object.

See also

arules::transactions, transactions2DF().

Other preparation: discretizeDF.supervised(), mineCARs(), transactions2DF()

Author

Michael Hahsler

Examples

# Perform discretization and convert to transactions
data("iris")
iris_trans <- prepareTransactions(Species ~ ., iris)

inspect(head(iris_trans))
#>     items                       transactionID
#> [1] {Sepal.Length=[-Inf,5.55),               
#>      Sepal.Width=[3.35, Inf],                
#>      Petal.Length=[-Inf,2.45),               
#>      Petal.Width=[-Inf,0.8),                 
#>      Species=setosa}                        1
#> [2] {Sepal.Length=[-Inf,5.55),               
#>      Sepal.Width=[2.95,3.35),                
#>      Petal.Length=[-Inf,2.45),               
#>      Petal.Width=[-Inf,0.8),                 
#>      Species=setosa}                        2
#> [3] {Sepal.Length=[-Inf,5.55),               
#>      Sepal.Width=[2.95,3.35),                
#>      Petal.Length=[-Inf,2.45),               
#>      Petal.Width=[-Inf,0.8),                 
#>      Species=setosa}                        3
#> [4] {Sepal.Length=[-Inf,5.55),               
#>      Sepal.Width=[2.95,3.35),                
#>      Petal.Length=[-Inf,2.45),               
#>      Petal.Width=[-Inf,0.8),                 
#>      Species=setosa}                        4
#> [5] {Sepal.Length=[-Inf,5.55),               
#>      Sepal.Width=[3.35, Inf],                
#>      Petal.Length=[-Inf,2.45),               
#>      Petal.Width=[-Inf,0.8),                 
#>      Species=setosa}                        5
#> [6] {Sepal.Length=[-Inf,5.55),               
#>      Sepal.Width=[3.35, Inf],                
#>      Petal.Length=[-Inf,2.45),               
#>      Petal.Width=[-Inf,0.8),                 
#>      Species=setosa}                        6
itemInfo(iris_trans)
#>                      labels    variables      levels
#> 1  Sepal.Length=[-Inf,5.55) Sepal.Length [-Inf,5.55)
#> 2  Sepal.Length=[5.55,6.15) Sepal.Length [5.55,6.15)
#> 3  Sepal.Length=[6.15, Inf] Sepal.Length [6.15, Inf]
#> 4   Sepal.Width=[-Inf,2.95)  Sepal.Width [-Inf,2.95)
#> 5   Sepal.Width=[2.95,3.35)  Sepal.Width [2.95,3.35)
#> 6   Sepal.Width=[3.35, Inf]  Sepal.Width [3.35, Inf]
#> 7  Petal.Length=[-Inf,2.45) Petal.Length [-Inf,2.45)
#> 8  Petal.Length=[2.45,4.75) Petal.Length [2.45,4.75)
#> 9  Petal.Length=[4.75, Inf] Petal.Length [4.75, Inf]
#> 10   Petal.Width=[-Inf,0.8)  Petal.Width  [-Inf,0.8)
#> 11   Petal.Width=[0.8,1.75)  Petal.Width  [0.8,1.75)
#> 12  Petal.Width=[1.75, Inf]  Petal.Width [1.75, Inf]
#> 13           Species=setosa      Species      setosa
#> 14       Species=versicolor      Species  versicolor
#> 15        Species=virginica      Species   virginica

# A negative class item is added for regular transaction data. Here we get the
# items "canned beer=TRUE" and "canned beer=FALSE".
# Note: backticks are needed in formulas with item labels that contain
# a space or special character.
data("Groceries")
g2 <- prepareTransactions(`canned beer` ~ ., Groceries)

inspect(head(g2))
#>     items                      
#> [1] {citrus fruit,             
#>      semi-finished bread,      
#>      margarine,                
#>      ready soups,              
#>      canned beer=FALSE}        
#> [2] {tropical fruit,           
#>      yogurt,                   
#>      coffee,                   
#>      canned beer=FALSE}        
#> [3] {whole milk,               
#>      canned beer=FALSE}        
#> [4] {pip fruit,                
#>      yogurt,                   
#>      cream cheese ,            
#>      meat spreads,             
#>      canned beer=FALSE}        
#> [5] {other vegetables,         
#>      whole milk,               
#>      condensed milk,           
#>      long life bakery product, 
#>      canned beer=FALSE}        
#> [6] {whole milk,               
#>      butter,                   
#>      yogurt,                   
#>      rice,                     
#>      abrasive cleaner,         
#>      canned beer=FALSE}        
ii <- itemInfo(g2)
ii[ii[["variables"]] == "canned beer", ]
#>                labels level2 level1   variables levels
#> 109  canned beer=TRUE   beer drinks canned beer   TRUE
#> 170 canned beer=FALSE   <NA>   <NA> canned beer  FALSE