- R in a Nutshell
- Preface
- I. R Basics
- II. The R Language
- III. Working with Data
- IV. Data Visualization
- V. Statistics with R
- VI. Additional Topics
- A. R Reference
- Bibliography
- Index
- About the Author
- Colophon
- Copyright

Association rules are a popular technique for data mining.
The association rule algorithm was developed initially by Rakesh Agrawal,
Tomasz Imielinski, and Arun Swami at the IBM Almaden Research
Center.^{[61]} It was originally designed as an efficient algorithm for
finding interesting relationships in large databases of customer
transactions. The algorithm finds sets of associations, items that are
frequently associated with each other. For example, when analyzing
supermarket data, you might find that consumers often purchase eggs and
milk together. The algorithm was designed to run efficiently on large
databases, especially databases that don’t fit into a computer’s
memory.

R includes several algorithms implementing association rules. One of
the most popular is the a priori algorithm. To try it in R, use the
`apriori`

function in the `arules`

package:

library(arules) apriori(data, parameter = NULL, appearance = NULL, control = NULL)

Here is a description of the arguments to `apriori`

.

Argument | Description | Default |
---|---|---|

data | An object of class `transactions` (or a matrix or data frame
that can be coerced into that form) in which associations are to
be found. | |

parameter | An object of class `ASParameter` (or a list with named
components) that is used to specify mining parameters. Parameters
include support level, minimum rule length, maximum rule length,
and types of rules (see the help file for `ASParameter` for more
information). | `NULL` |

appearance | An object of class `APappearance` (or a list with ... |