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 item)

`maxlen`:

  an integer value for the maximal number of items per item set (default: 10 items)

`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; use `ruleInduction` for `eclat`.)

  - `"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). For frequent itemsets it is set to `NA`.

`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 χ^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`)

`maxtime`:

  Time limit in seconds for checking subsets. `maxtime=0` disables the time limit. (default: 5 seconds)

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")`

### Author(s)

Michael Hahsler and Bettina Gruen

### References

Christian Borgelt (2004) Apriori — Finding Association Rules/Hyperedges with the Apriori Algorithm. www.borgelt.net/apriori.html

### See Also

`apriori`, `eclat`, `weclat` (for weighted rule mining), `ruleInduction`

[Package arules version 1.6-3 Index]