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<title>Data::Mining:AssociationRules - Mine association rules and frequent
sets from data.</title>
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<p><a name="__index__"></a></p>
<!-- INDEX BEGIN -->
<ul>
<li><a href="#name">NAME</a></li>
<li><a href="#synopsis">SYNOPSIS</a></li>
<li><a href="#installation">INSTALLATION</a></li>
<li><a href="#functions">FUNCTIONS</a></li>
<ul>
<li><a href="#read_transaction_file__transaction_map_ref___transaction_file_">read_transaction_file($transaction_map_ref, $transaction_file)</a></li>
<li><a href="#generate_frequent_sets___transaction_map_ref___file_prefix___support_threshold___max_n_">generate_frequent_sets ($transaction_map_ref, $file_prefix, $support_threshold, $max_n)</a></li>
<li><a href="#read_frequent_sets__set_map_ref___file_prefix_">read_frequent_sets($set_map_ref, $file_prefix)</a></li>
<li><a href="#generate_rules__file_prefix___support_threshold___max_n_">generate_rules($file_prefix, $support_threshold, $max_n)</a></li>
</ul>
<li><a href="#description">DESCRIPTION</a></li>
<ul>
<li><a href="#frequent_sets">FREQUENT SETS</a></li>
<ul>
<li><a href="#the_detail">The detail</a></li>
</ul>
<li><a href="#rules">RULES</a></li>
<ul>
<li><a href="#the_detail">The detail</a></li>
<li><a href="#frequent_sets_and_association_rules_generally_useful">FREQUENT SETS AND ASSOCIATION RULES GENERALLY USEFUL</a></li>
</ul>
</ul>
<li><a href="#examples">EXAMPLES</a></li>
<li><a href="#algorithm">ALGORITHM</a></li>
<ul>
<li><a href="#generating_frequent_sets">Generating frequent sets</a></li>
<li><a href="#generating_rules">Generating rules</a></li>
</ul>
<li><a href="#bugs">BUGS</a></li>
<li><a href="#version">VERSION</a></li>
<li><a href="#author">AUTHOR</a></li>
<li><a href="#license">LICENSE</a></li>
<li><a href="#disclaimer">DISCLAIMER</a></li>
</ul>
<!-- INDEX END -->
<hr />
<p>
</p>
<h1><a name="name">NAME</a></h1>
<p>Data::Mining:AssociationRules - Mine association rules and frequent
sets from data.</p>
<p>
</p>
<hr />
<h1><a name="synopsis">SYNOPSIS</a></h1>
<pre>
use Data::Mining::AssociationRules;</pre>
<pre>
my %transaction_map;
my $transaction_file = "foo.txt";</pre>
<pre>
read_transaction_file(\%transaction_map, $transaction_file);</pre>
<pre>
generate_frequent_sets(\%transaction_map, $output_file_prefix,
$support_threshold, $max_n);</pre>
<pre>
generate_rules($output_file_prefix, $support_threshold,
$confidence_threshold, $max_n);</pre>
<pre>
read_frequent_sets($set_map_ref, $file_prefix)</pre>
<pre>
set_debug(1);</pre>
<pre>
perl arm.pl -transaction-file foo.txt -support 2 -confidence-threshold 0.01 -max-set-size 6</pre>
<pre>
See also FUNCTIONS, DESCRIPTION, and EXAMPLES below.</pre>
<p>
</p>
<hr />
<h1><a name="installation">INSTALLATION</a></h1>
<p>The typical:</p>
<ol>
<li><strong><a name="item_perl_makefile_2epl">perl Makefile.PL</a></strong><br />
</li>
<li><strong><a name="item_make_test">make test</a></strong><br />
</li>
<li><strong><a name="item_make_install">make install</a></strong><br />
</li>
</ol>
<p>
</p>
<hr />
<h1><a name="functions">FUNCTIONS</a></h1>
<p>
</p>
<h2><a name="read_transaction_file__transaction_map_ref___transaction_file_">read_transaction_file($transaction_map_ref, $transaction_file)</a></h2>
<p>Read in a transaction map from a file which has lines of two
whitespace-separated columns:</p>
<pre>
transaction-id item-id</pre>
<p>
</p>
<h2><a name="generate_frequent_sets___transaction_map_ref___file_prefix___support_threshold___max_n_">generate_frequent_sets ($transaction_map_ref, $file_prefix, $support_threshold, $max_n)</a></h2>
<p>Given</p>
<ol>
<li><strong><a name="item_a_map_of_transactions">a map of transactions</a></strong><br />
</li>
<li><strong><a name="item_a_file_prefix">a file prefix</a></strong><br />
</li>
<li><strong><a name="item_a_support_threshold">a support threshold</a></strong><br />
</li>
<li><strong><a name="item_for">a maximum frequent set size to look for (optional)</a></strong><br />
</li>
</ol>
<p>generate the frequent sets in some files, one file per size of the set.
That is, all 1-sets are in a file, all 2-sets in another, etc.</p>
<p>The files are lines of the form:</p>
<pre>
support-count item-set</pre>
<p>where</p>
<ol>
<li><strong><a name="item_support_2dcount_is_the_number_of_transactions_in_w">support-count is the number of transactions in which the item-set appears</a></strong><br />
</li>
<li><strong><a name="item_item_2dset_is_one_or_more_space_2dseparated_items">item-set is one or more space-separated items</a></strong><br />
</li>
</ol>
<p>
</p>
<h2><a name="read_frequent_sets__set_map_ref___file_prefix_">read_frequent_sets($set_map_ref, $file_prefix)</a></h2>
<p>Given</p>
<ol>
<li><strong><a name="item_a_set_map">a set map</a></strong><br />
</li>
<li><strong>a file prefix</strong><br />
</li>
<li><strong><a name="item_support_threshold">support threshold</a></strong><br />
</li>
<li><strong><a name="item_size">max frequent set size (optional)</a></strong><br />
</li>
</ol>
<p>read all the frequent sets into a single map, which has as its key the
frequent set (joined by single spaces) and as its value the support.</p>
<p>
</p>
<h2><a name="generate_rules__file_prefix___support_threshold___max_n_">generate_rules($file_prefix, $support_threshold, $max_n)</a></h2>
<p>Given</p>
<ol>
<li><strong>a file prefix</strong><br />
</li>
<li><strong><a name="item_threshold">a support threshold (optional)</a></strong><br />
</li>
<li><strong>a confidence threshold (optional)</strong><br />
</li>
<li><strong>maximum frequent set size to look for (optional)</strong><br />
</li>
</ol>
<p>create a file with all association rules in it. The output file is of
the form:</p>
<pre>
support-count confidence left-hand-set-size right-hand-set-size frequent-set-size left-hand-set => right-hand-set</pre>
<p>
</p>
<hr />
<h1><a name="description">DESCRIPTION</a></h1>
<p>This module contains some functions to do association rule mining from
text files. This sounds obscure, but really measures beautifully
simple things through counting.</p>
<p>
</p>
<h2><a name="frequent_sets">FREQUENT SETS</a></h2>
<p>Frequent sets answer the question, ``Which events occur together more
than N times?''</p>
<p>
</p>
<h3><a name="the_detail">The detail</a></h3>
<p>The 'transaction file' contains items in transactions. A set of items
has 'support' s if all the items occur together in at least s
transactions. (In many papers, support is a number between 0 and 1
representing the fraction of total transactions. I found the absolute
number itself more interesting, so I use that instead. Sorry for the
confusion.) For an itemset ``A B C'', the support is sometimes notated
``T(A B C)'' (the number of 'T'ransactions).</p>
<p>A set of items is called a 'frequent set' if it has support at least
the given support threshold. Generating frequent set produces all
frequent sets, and some information about each set (e.g., its
support).</p>
<p>
</p>
<h2><a name="rules">RULES</a></h2>
<p>Association rules answer the (related) question, ``When these events
occur, how often do those events also occur?''</p>
<p>
</p>
<h3><a name="the_detail">The detail</a></h3>
<p>A rule has a left-hand set of items and a right-hand set
of items. A rule ``LHS => RHS'' with a support s and 'confidence' c means
that the underlying frequent set (LHS + RHS) occured together in at
least s transactions, and for all the transactions LHS occurred in,
RHS also occured in at least the fraction c (a number from 0 to 1).</p>
<p>Generating rules produces all rules with support at least the given
support threshold, and confidence at least the given confidence
threshold. The confidence is sometimes notated ``conf(LHS => RHS) =
T(LHS + RHS) / T(LHS)''. There is also related data with each rule
(e.g., the size of its LHS and RHS, the support, the confidence,
etc.).</p>
<p>
</p>
<h3><a name="frequent_sets_and_association_rules_generally_useful">FREQUENT SETS AND ASSOCIATION RULES GENERALLY USEFUL</a></h3>
<p>Although association rule mining is often described in commercial
terms like ``market baskets'' or ``transactions'' (collections of events)
and ``items'' (events), one can imagine events that make this sort of
counting useful across many domains. Events could be</p>
<ol>
<li><strong><a name="item_stock_market_went_down_at_time_t">stock market went down at time t</a></strong><br />
</li>
<li><strong><a name="item_patient_had_symptom_x">patient had symptom X</a></strong><br />
</li>
<li><strong><a name="item_flower_petal_length_was__3e_5mm">flower petal length was > 5mm</a></strong><br />
</li>
</ol>
<p>For this reason, I believe counting frequent sets and looking at
association rules to be a fundamental tool of any data miner, someone
who is looking for patterns in pre-existing data, whether commercial
or not.</p>
<p>
</p>
<hr />
<h1><a name="examples">EXAMPLES</a></h1>
<p>Given the following input file:</p>
<pre>
234 Orange
463 Strawberry
53 Apple
234 Banana
412 Peach
467 Pear
234 Pear
147 Pear
141 Orange
375 Orange</pre>
<p>Generating frequent sets at support threshold 1 (a.k.a. 'at support 1')
produces three files:</p>
<p>The 1-sets:</p>
<pre>
1 Strawberry
1 Banana
1 Apple
3 Orange
1 Peach
3 Pear</pre>
<p>The 2-sets:</p>
<pre>
1 Banana Orange
1 Banana Pear
1 Orange Pear</pre>
<p>The 3-sets:</p>
<pre>
1 Banana Orange Pear</pre>
<p>Generating the rules at support 1 produces the following:</p>
<pre>
1 0.333 1 1 2 Orange => Pear
1 0.333 1 1 2 Pear => Orange
1 1.000 1 2 3 Banana => Orange Pear
1 0.333 1 2 3 Orange => Banana Pear
1 1.000 2 1 3 Banana Orange => Pear
1 0.333 1 2 3 Pear => Banana Orange
1 1.000 2 1 3 Banana Pear => Orange
1 1.000 2 1 3 Orange Pear => Banana
1 1.000 1 1 2 Banana => Orange
1 0.333 1 1 2 Orange => Banana
1 1.000 1 1 2 Banana => Pear
1 0.333 1 1 2 Pear => Banana</pre>
<p>Generating frequent sets at support 2 produces one file:</p>
<pre>
3 Orange
3 Pear</pre>
<p>Generating rules at support 2 produces nothing.</p>
<p>Generating rules at support 1 and confidence 0.5 produces:</p>
<pre>
1 1.000 1 2 3 Banana => Orange Pear
1 1.000 2 1 3 Banana Orange => Pear
1 1.000 2 1 3 Banana Pear => Orange
1 1.000 2 1 3 Orange Pear => Banana
1 1.000 1 1 2 Banana => Orange
1 1.000 1 1 2 Banana => Pear</pre>
<p>Note all the lower confidence rules are gone.</p>
<p>
</p>
<hr />
<h1><a name="algorithm">ALGORITHM</a></h1>
<p>
</p>
<h2><a name="generating_frequent_sets">Generating frequent sets</a></h2>
<p>Generating frequent sets is straight-up Apriori. See for example:</p>
<p><a href="http://www.almaden.ibm.com/software/quest/Publications/papers/vldb94_rj.pdf">http://www.almaden.ibm.com/software/quest/Publications/papers/vldb94_rj.pdf</a></p>
<p>I have not optimized. It depends on having the transactions all in
memory. However, given that, it still might scale decently (millions
of transactions).</p>
<p>
</p>
<h2><a name="generating_rules">Generating rules</a></h2>
<p>Generating rules is a very vanilla implementation. It requires
reading all the frequent sets into memory, which does not scale at
all. Given that, since computers have lots of memory these days, you
might still be able to get away with millions of frequent sets (which
is <<millions of transactions).</p>
<p>
</p>
<hr />
<h1><a name="bugs">BUGS</a></h1>
<p>There is an existing tool (written in C) to mine frequent sets I kept
running across:</p>
<p><a href="http://fuzzy.cs.uni-magdeburg.de/~borgelt/software.html#assoc">http://fuzzy.cs.uni-magdeburg.de/~borgelt/software.html#assoc</a></p>
<p>I should check it out to see if it is easy or desirable to be
file-level compatible with it.</p>
<p>One could imagine wrapping it in Perl, but the Perl-C/C++ barrier is
where I have encountered all my troubles in the past, so I wouldn't
personally pursue that.</p>
<p>
</p>
<hr />
<h1><a name="version">VERSION</a></h1>
<p>This document describes Data::Mining::AssociationRules version 0.1.</p>
<p>
</p>
<hr />
<h1><a name="author">AUTHOR</a></h1>
<pre>
Dan Frankowski
dfrankow@winternet.com
<a href="http://www.winternet.com/~dfrankow">http://www.winternet.com/~dfrankow</a></pre>
<pre>
Hey, if you download this module, drop me an email! That's the fun
part of this whole open source thing.</pre>
<p>
</p>
<hr />
<h1><a name="license">LICENSE</a></h1>
<p>This program is free software; you can redistribute it and/or modify
it under the same terms as Perl itself.</p>
<p>The full text of the license can be found in the LICENSE file included
in the distribution and available in the CPAN listing for
Data::Mining::AssociationRules (see www.cpan.org or search.cpan.org).</p>
<p>
</p>
<hr />
<h1><a name="disclaimer">DISCLAIMER</a></h1>
<p>To the maximum extent permitted by applicable law, the author of this
module disclaims all warranties, either express or implied, including
but not limited to implied warranties of merchantability and fitness
for a particular purpose, with regard to the software and the
accompanying documentation.</p>
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