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<html><head><title>R: Computing Affinity Between Items</title>
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<table width="100%" summary="page for affinity {arules}"><tr><td>affinity {arules}</td><td align="right">R Documentation</td></tr></table>
<h2>Computing Affinity Between Items</h2>
<h3>Description</h3>
<p>
Provides the generic function <code>affinity</code> and the S4 methods
to compute and return a similarity matrix with the affinities between items
for a set of <code><a href="transactions-class.html">transactions</a></code>.
</p>
<h3>Usage</h3>
<pre>
affinity(x)
</pre>
<h3>Arguments</h3>
<table summary="R argblock">
<tr valign="top"><td><code>x</code></td>
<td>
a matrix or an object of class <code>itemMatrix</code>
or <code>transactions</code>.</td></tr>
</table>
<h3>Details</h3>
<p>
Affinity between the two items <i>i</i> and <i>j</i> is defined
by Aggarwal et al. (2002) as
</p><p align="center"><i>A(i,j) = sup({i,j})/(sup({i}) + sup({j}) - sup({i,j})),</i></p><p>
where <i>sup(.)</i> is the support measure. This means that affinity is the
<EM>Jaccard similarity</EM> between items.
</p>
<h3>Value</h3>
<p>
returns an object of class <code>ar_similarity</code> which represents
the affinities between items in <code>x</code>.</p>
<h3>References</h3>
<p>
Charu C. Aggarwal, Cecilia Procopiuc, and Philip S. Yu (2002)
Finding localized associations in market basket data,
<EM>IEEE Trans. on Knowledge and Data Engineering,</EM> 14(1):51–62.
</p>
<h3>See Also</h3>
<p>
<code><a href="dissimilarity.html">dissimilarity</a></code>,
<code><a href="proximity-classes.html">ar_similarity-class</a></code>,
<code><a href="itemMatrix-class.html">itemMatrix-class</a></code>
</p>
<h3>Examples</h3>
<pre>
data("Adult")
## choose a sample, calculate affinities
s <- sample(Adult, 500)
s
a <- affinity(s)
summary(as.vector(a))
</pre>
<hr><div align="center">[Package <em>arules</em> version 0.6-6 <a href="00Index.html">Index]</a></div>
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