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<table width="100%" summary="page for dissimilarity {arules}"><tr><td>dissimilarity {arules}</td><td align="right">R Documentation</td></tr></table>
<h2>Dissimilarity Computation</h2>
<h3>Description</h3>
<p>
Provides the generic function <code>dissimilarity</code> and the S4 methods to
compute and returns distances for binary data in a <code>matrix</code>,
<code><a href="transactions-class.html">transactions</a></code> or <code><a href="associations-class.html">associations</a></code>.
</p>
<h3>Usage</h3>
<pre>
dissimilarity(x, y = NULL, method = NULL, args = NULL, ...)
## S4 method for signature 'itemMatrix':
dissimilarity(x, y = NULL, method = NULL, args = NULL,
which = "transactions")
## S4 method for signature 'associations':
dissimilarity(x, y = NULL, method = NULL, args = NULL,
which = "transactions")
## S4 method for signature 'matrix':
dissimilarity(x, y = NULL, method = NULL, args = NULL)
</pre>
<h3>Arguments</h3>
<table summary="R argblock">
<tr valign="top"><td><code>x</code></td>
<td>
the set of elements (e.g., <code>matrix, itemMatrix, transactions,
itemsets, rules</code>). </td></tr>
<tr valign="top"><td><code>y</code></td>
<td>
<code>NULL</code> or a second set to calculate cross dissimilarities. </td></tr>
<tr valign="top"><td><code>method</code></td>
<td>
the distance measure to be used. Implemented measures
are (defaults to <code>"jaccard"</code>):
<dl>
<dt><code>"jaccard"</code>:</dt><dd>the number of items which occur in both
elements divided by the total number of items in the
elements (Sneath, 1957).
This measure is often
also called: <EM>binary, asymmetric binary,</EM> etc. </dd>
<dt><code>"matching"</code>:</dt><dd>the <EM>Matching coefficient</EM> defined
by Sokal and Michener (1958). This coefficient gives the same
weigth to presents and absence of items.</dd>
<dt><code>"dice"</code>:</dt><dd>the <EM>Dice's coefficient</EM> defined
by Dice (1945). Similar to <EM>Jaccard</EM> but gives double the
weight to agreeing items.</dd>
<dt><code>"cosine"</code>:</dt><dd>the <EM>cosine</EM> distance.</dd>
<dt><code>"affinity"</code>:</dt><dd>measure based on
the <code><a href="affinity.html">affinity</a></code></dd>,
a similarity measure between items. It is defined as the
average <EM>affinity</EM> between the items in two transactions
(see Aggarwal et al. (2002)).</dl>
</td></tr>
<tr valign="top"><td><code>args</code></td>
<td>
a list of additional arguments for the methods.
<br>
For calculating
<code>"affinity"</code> for associations, the affinities between the items in
the transactions are needed and passed to the method as the first
element in <code>args</code>.</td></tr>
<tr valign="top"><td><code>which</code></td>
<td>
a character string indicating if the dissimilarity should be
calculated between transavtions (default) or items (use <code>"items"</code>). </td></tr>
<tr valign="top"><td><code>...</code></td>
<td>
further arguments.</td></tr>
</table>
<h3>Value</h3>
<p>
returns an object of class <code>dist</code>.</p>
<h3>References</h3>
<p>
Sneath, P. H. A. (1957) Some thoughts on bacterial classification.
<EM>Journal of General Microbiology</EM> 17, pages 184–200.
</p>
<p>
Sokal, R. R. and Michener, C. D. (1958) A statistical method for evaluating
systematic relationships. <EM>University of Kansas Science Bulletin</EM> 38,
pages 1409–1438.
</p>
<p>
Dice, L. R. (1945) Measures of the amount of ecologic association
between species. <EM>Ecology</EM> 26, pages 297–302.
</p>
<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="affinity.html">affinity</a></code>,
<code><a href="proximity-classes.html">dist-class</a></code>,
<code><a href="itemMatrix-class.html">itemMatrix-class</a></code>,
<code><a href="associations-class.html">associations-class</a></code>.
</p>
<h3>Examples</h3>
<pre>
## cluster items in Groceries with support > 5%
data("Groceries")
s <- Groceries[,itemFrequency(Groceries)>0.05]
d_jaccard <- dissimilarity(s, which = "items")
plot(hclust(d_jaccard, method = "ward"))
## cluster transactions for a sample of Adult
data("Adult")
s <- sample(Adult, 200)
## calculate Jaccard distances and do hclust
d_jaccard <- dissimilarity(s)
plot(hclust(d_jaccard))
## calculate affinity-based distances and do hclust
d_affinity <- dissimilarity(s, method = "affinity")
plot(hclust(d_affinity))
## cluster rules
rules <- apriori(Adult)
rules <- subset(rules, subset = lift > 2)
## we need to supply the item affinities from the dataset (sample)
d_affinity <- dissimilarity(rules, method = "affinity",
args = list(affinity = affinity(s)))
plot(hclust(d_affinity))
</pre>
<hr><div align="center">[Package <em>arules</em> version 0.6-6 <a href="00Index.html">Index]</a></div>
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