# ruleInduction-methods

##### Induce Sequence Rules

Induce a set of strong sequence rules from a set of frequent sequences, i.e. which (1) satisfy the minimum confidence threshold and (2) which contain the last element of the generating sequence as the right-hand side (consequent) sequence.

- Keywords
- models

##### Usage

```
# S4 method for sequences
ruleInduction(x, transactions, confidence = 0.8, control = NULL)
```

##### Arguments

- x
an object.

- transactions
an optional object of class

`transactions`

with temporal information.- confidence
a numeric value specifying the minimum confidence threshold.

- control
a named list with logical component

`verbose`

specifying if progress and runtime information should be displayed.

##### Details

If `transactions`

is not specified, the collection of sequences
supplied must be closed with respect to the rules to be induced. That
is, the left- and the right-hand side sequence of each candidate rule
must be contained in the collection of sequences. However, using timing
constraints in the mining step the set of frequent sequences may not be
closed under rule induction.

Otherwise, `x`

is completed (augmented) to be closed under rule
induction and the support is computed from `transactions`

, using
method ptree. Note that, rules for added sequences, if any, are not
induced.

##### Value

##### See Also

Class
`'>sequences`

,
`'>sequencerules`

,
method
`support`

,
function
`cspade`

.

##### Examples

```
# NOT RUN {
## continue example
example(cspade)
## mine rules
r2 <- ruleInduction(s2, confidence = 0.5,
control = list(verbose = TRUE))
summary(r2)
as(r2, "data.frame")
# }
```

*Documentation reproduced from package arulesSequences, version 0.2-19, License: GPL-2*