# combine: Combining Objects In arules: Mining Association Rules and Frequent Itemsets

## Description

Provides the S4 methods to combine several objects based on `itemMatrix` into a single object.

Note, use `union` rather than `c` to combine several mined `itemsets` (or `rules`) into a single set.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```## S4 method for signature 'itemMatrix' c(x, ..., recursive = FALSE) ## S4 method for signature 'transactions' c(x, ..., recursive = FALSE) ## S4 method for signature 'rules' c(x, ..., recursive = FALSE) ## S4 method for signature 'itemsets' c(x, ..., recursive = FALSE) ```

## Arguments

 `x` first object. `...` further objects of the same class as `x` to be combined. `recursive` a logical. If `recursive=TRUE`, the function recursively descends through lists combining all their elements into a vector.

## Value

An object of the same class as `x`.

## Author(s)

Michael Hahsler

`itemMatrix-class`, `transactions-class`, `rules-class`, `itemsets-class`

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18``` ```data("Adult") ## combine transactions a1 <- Adult[1:10] a2 <- Adult[101:110] aComb <- c(a1, a2) summary(aComb) ## combine rules (can contain the same rule multiple times) r1 <- apriori(Adult[1:1000]) r2 <- apriori(Adult[1001:2000]) rComb <- c(r1, r2) rComb ## union of rules (a set with only unique rules: same as unique(rComb)) rUnion <- union(r1,r2) rUnion ```

### Example output

```Loading required package: Matrix

Attaching package: 'arules'

The following objects are masked from 'package:base':

abbreviate, write

transactions as itemMatrix in sparse format with
20 rows (elements/itemsets/transactions) and
115 columns (items) and a density of 0.1121739

most frequent items:
capital-loss=None native-country=United-States
20                           18
race=White            capital-gain=None
17                           14
hours-per-week=Full-time                      (Other)
14                          175

element (itemset/transaction) length distribution:
sizes
11 13
1 19

Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
11.0    13.0    13.0    12.9    13.0    13.0

includes extended item information - examples:
labels variables      levels
1       age=Young       age       Young
2 age=Middle-aged       age Middle-aged
3      age=Senior       age      Senior

includes extended transaction information - examples:
transactionID
1             1
2             2
3             3
Apriori

Parameter specification:
confidence minval smax arem  aval originalSupport maxtime support minlen
0.8    0.1    1 none FALSE            TRUE       5     0.1      1
maxlen target   ext
10  rules FALSE

Algorithmic control:
filter tree heap memopt load sort verbose
0.1 TRUE TRUE  FALSE TRUE    2    TRUE

Absolute minimum support count: 100

set item appearances ...[0 item(s)] done [0.00s].
set transactions ...[100 item(s), 1000 transaction(s)] done [0.00s].
sorting and recoding items ... [31 item(s)] done [0.00s].
creating transaction tree ... done [0.00s].
checking subsets of size 1 2 3 4 5 6 7 8 done [0.01s].
writing ... [8500 rule(s)] done [0.00s].
creating S4 object  ... done [0.00s].
Apriori

Parameter specification:
confidence minval smax arem  aval originalSupport maxtime support minlen
0.8    0.1    1 none FALSE            TRUE       5     0.1      1
maxlen target   ext
10  rules FALSE

Algorithmic control:
filter tree heap memopt load sort verbose
0.1 TRUE TRUE  FALSE TRUE    2    TRUE

Absolute minimum support count: 100

set item appearances ...[0 item(s)] done [0.00s].
set transactions ...[101 item(s), 1000 transaction(s)] done [0.00s].
sorting and recoding items ... [30 item(s)] done [0.00s].
creating transaction tree ... done [0.00s].
checking subsets of size 1 2 3 4 5 6 7 8 9 done [0.01s].
writing ... [8575 rule(s)] done [0.00s].
creating S4 object  ... done [0.00s].
set of 17075 rules
set of 9928 rules
```

arules documentation built on April 7, 2018, 9:03 a.m.