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<table width="100%" summary="page for support {arules}"><tr><td>support {arules}</td><td align="right">R Documentation</td></tr></table>
<h2>Support Counting for Itemsets</h2>
<h3>Description</h3>
<p>
Provides the generic function and the needed S4 method to count support for
given itemsets (and other types of associations) in a given transaction
database.
</p>
<h3>Usage</h3>
<pre>
support(x, transactions, ...)
## S4 method for signature 'itemMatrix':
support(x, transactions,
type= c("relative", "absolute"), control = NULL)
## S4 method for signature 'associations':
support(x, transactions,
type= c("relative", "absolute"), control = NULL)
</pre>
<h3>Arguments</h3>
<table summary="R argblock">
<tr valign="top"><td><code>x</code></td>
<td>
the set of itemsets for which support should be counted. </td></tr>
<tr valign="top"><td><code>...</code></td>
<td>
further arguments are passed on. </td></tr>
<tr valign="top"><td><code>transactions</code></td>
<td>
the transaction data set used for mining. </td></tr>
<tr valign="top"><td><code>type</code></td>
<td>
a character string specifying
if <code>"relative"</code> support or <code>"absolute"</code>
support (counts) are returned for the itemsets in <code>x</code>.
(default: <code>"relative"</code>)</td></tr>
<tr valign="top"><td><code>control</code></td>
<td>
a named list with elements
<code>method</code> indicating the method (<code>"tidlists"</code> or <code>"ptree"</code>),
and the logical arguments
<code>reduce</code> and
<code>verbose</code> to indicate if unused items are removed and if
the output should be verbose.</td></tr>
</table>
<h3>Details</h3>
<p>
Normally, itemset support is counted during mining the database
with a set minimum support. However, if only the support information
for a single or a few itemsets is needed, one might not want to
mine the database for all frequent itemsets.
</p>
<p>
If in control <code>method = "ptree"</code> is used,
the counters for the itemsets are
organized in a prefix tree. The transactions are sequencially processed
and the corresponding counters in the prefix tree are incremented
(see Hahsler et al, 2008). This method is used by default since it is
typically significantly faster than tid list intersection.
</p>
<p>
If in control <code>method = "tidlists"</code> is used,
support is counted using transaction ID list intersection
which is used by several fast mining algorithms
(e.g., by Eclat). However, Support is determined for each itemset
individually which is slow for a large number of long itemsets
in dense data.
</p>
<p>
If in control <code>reduce = TRUE</code> is used, unused items are removed from
the data before creating rules. This might be slower for large transaction
data sets.
</p>
<h3>Value</h3>
<p>
A numeric vector of the same length as <code>x</code> containing
the support values for the sets in <code>x</code>.</p>
<h3>References</h3>
<p>
Michael Hahsler, Christian Buchta, and Kurt Hornik. Selective association
rule generation. <EM>Computational Statistics</EM>, 23(2):303-315, April 2008.
</p>
<h3>See Also</h3>
<p>
<code><a href="itemMatrix-class.html">itemMatrix-class</a></code>,
<code><a href="associations-class.html">associations-class</a></code>,
<code><a href="transactions-class.html">transactions-class</a></code>
</p>
<h3>Examples</h3>
<pre>
data("Income")
## find and some frequent itemsets
itemsets <- eclat(Income)[1:5]
## inspect the support returned by eclat
inspect(itemsets)
## count support in the database
support(items(itemsets), Income)
</pre>
<hr><div align="center">[Package <em>arules</em> version 0.6-6 <a href="00Index.html">Index]</a></div>
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