ASparameter-classes {arules} | R Documentation

## Classes “ASparameter”, “APparameter”, “ECparameter” — Specifying the 'parameter' Argument of apriori() and eclat()

### Description

The `ASparameter` class holds the mining parameters (e.g., minimum support) for the used mining algorithms. `APparameter` and `ECparameter` directly extend `ASparameter` with additional slots for parameters only suitable for the Apriori (`APparameter`) or the Eclat algorithms (`ECparameter`).

### Objects from the Class

A suitable default parameter object will be automatically created by the `apriori` or the `eclat` function. By specifying a named list (names equal to slots) as `parameter` argument for the `apriori` or the `eclat` function, default values can be replaced by the values in the list. Objects can be created by calls of the form `new("APparameter", ...)` or `new("ECparameter", ...)`.

### Slots

Common slots defined in `ASparameter`:

`support`:
  a numeric value for the minimal support of an item set (default: 0.1)
`minlen`:
  an integer value for the minimal number of items per item set (default: 1)
`maxlen`:
  an integer value for the maximal number of items per item set (default: 5)
`target`:
  a character string indicating the type of association mined. One of
  - `"frequent itemsets"`
  - `"maximally frequent itemsets"`
  - `"closed frequent itemsets"`
  - `"rules"` (only available for Apriori)
  - `"hyperedgesets"` (only available for Apriori; see references for the definition of association hyperedgesets)
`ext`:
  a logical indicating whether to produce extended information on quality measures (e.g., lhs.support) (default: `FALSE`)

Additional slots for Apriori in `APparameter`:

`confidence`:
  a numeric value for the minimal confidence of rules/association hyperedges (default: 0.8)
`smax`:
  a numeric value for the maximal support of itemsets/rules/hyperedgesets (default: 1)
`arem`:
  a character string indicating the used additional rule evaluation measure (default: `"none"`) given by one of
  `"none"`:
    no additional evaluation measure
  `"diff"`:
    absolute confidence difference
  `"quot"`:
    difference of confidence quotient to 1
  `"aimp"`:
    absolute difference of improvement to 1
  `"info"`:
    information difference to prior
  `"chi2"`:
    normalized chi^2 measure
`aval`:
  a logical indicating whether to return the additional rule evaluation measure selected with `arem`.
`minval`:
  a numeric value for the minimal value of additional evaluation measure selected with `arem` (default: 0.1)
`originalSupport`:
  a logical indicating whether to use for minimum support the original definition of the support of a rule (lhs and rhs) instead of lhs support. Make sure to use `ext = TRUE` if `originalSupport` is set to `FALSE` (default: `TRUE`)

Additional slots for Eclat in `ECparameter`:

`tidLists`:
  a logical indicating whether to return also a list of supporting transactions (transaction IDs) (default: `FALSE`)

### Methods

coerce
  `signature(from = "NULL", to = "APparameter")`
coerce
  `signature(from = "list", to = "APparameter")`
coerce
  `signature(from = "NULL", to = "ECparameter")`
coerce
  `signature(from = "list", to = "ECparameter")`
show
  `signature(object = "ASparameter")`

### References

Christian Borgelt (2004) Apriori — Finding Association Rules/Hyperedges with the Apriori Algorithm. http://fuzzy.cs.uni-magdeburg.de/~borgelt/apriori.html

### See Also

`apriori`, `eclat`

[Package arules version 0.6-3 Index]