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<table width="100%" summary="page for apriori {arules}"><tr><td>apriori {arules}</td><td align="right">R Documentation</td></tr></table>
<h2>Mining Associations with Apriori</h2>
<h3>Description</h3>
<p>
Mine frequent itemsets, association rules or association hyperedges
using the Apriori algorithm. The Apriori algorithm employs level-wise
search for frequent itemsets. The implementation of Apriori used
includes some improvements (e.g., a prefix tree and item sorting).
</p>
<h3>Usage</h3>
<pre>
apriori(data, parameter = NULL, appearance = NULL, control = NULL)
</pre>
<h3>Arguments</h3>
<table summary="R argblock">
<tr valign="top"><td><code>data</code></td>
<td>
object of class
<code><a href="transactions-class.html">transactions</a></code> or any data structure
which can be coerced into
<code><a href="transactions-class.html">transactions</a></code> (e.g., a binary
matrix or data.frame).</td></tr>
<tr valign="top"><td><code>parameter</code></td>
<td>
object of class
<code><a href="ASparameter-class.html">APparameter</a></code> or named list.
The default behavior is to mine rules with support 0.1, confidence
0.8, and maxlen 5.</td></tr>
<tr valign="top"><td><code>appearance</code></td>
<td>
object of class
<code><a href="APappearance-class.html">APappearance</a></code> or named list.
With this argument item appearance can be restricted.
By default all items can appear unrestricted.</td></tr>
<tr valign="top"><td><code>control</code></td>
<td>
object of class
<code><a href="AScontrol-class.html">APcontrol</a></code> or named list.
Controls the performance of the mining algorithm (item sorting, etc.)</td></tr>
</table>
<h3>Details</h3>
<p>
Calls the C implementation of the Apriori algorithm by Christian
Borgelt for mining frequent itemsets, rules or hyperedges.
</p>
<h3>Value</h3>
<p>
Returns an object of class <code><a href="rules-class.html">rules</a></code> or
<code><a href="itemsets-class.html">itemsets</a></code>.</p>
<h3>References</h3>
<p>
R. Agrawal, T. Imielinski, and A. Swami (1993) Mining association rules
between sets of items in large databases. In <EM>Proceedings of the
ACM SIGMOD International Conference on Management of Data</EM>, pages
207–216,
Washington D.C.
</p>
<p>
Christian Borgelt and Rudolf Kruse (2002) Induction of Association Rules:
Apriori Implementation. <EM>15th Conference on Computational
Statistics</EM> (COMPSTAT 2002, Berlin, Germany) Physica Verlag,
Heidelberg, Germany.
</p>
<p>
Christian Borgelt (2003) Efficient Implementations of Apriori and
Eclat. <EM>Workshop of Frequent Item Set Mining Implementations</EM>
(FIMI 2003, Melbourne, FL, USA).
</p>
<h3>See Also</h3>
<p>
<code><a href="ASparameter-class.html">APparameter-class</a></code>,
<code><a href="AScontrol-class.html">APcontrol-class</a></code>,
<code><a href="APappearance-class.html">APappearance-class</a></code>,
<code><a href="transactions-class.html">transactions-class</a></code>,
<code><a href="itemsets-class.html">itemsets-class</a></code>,
<code><a href="rules-class.html">rules-class</a></code>
</p>
<h3>Examples</h3>
<pre>
data("Adult")
## Mine association rules.
rules <- apriori(Adult,
parameter = list(supp = 0.5, conf = 0.9,
target = "rules"))
summary(rules)
</pre>
<hr><div align="center">[Package <em>arules</em> version 0.6-6 <a href="00Index.html">Index]</a></div>
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