NAME

Data::Mining::Apriori - Perl extension for implement the apriori algorithm of data mining.

SYNOPSIS

use strict;
use warnings;
use Data::Mining::Apriori;

# TRANSACTION 103:CEREAL 101:MILK 102:BREAD
#        1101          1        1         0
#        1102          1        0         1
#        1103          1        1         1
#        1104          1        1         1
#        1105          0        1         1
#        1106          1        1         1
#        1107          1        1         1
#        1108          1        0         1
#        1109          1        1         1
#        1110          1        1         1

my $apriori = new Data::Mining::Apriori;

$apriori->{minSupport}=1.55; # The minimum support, default values is 1(percent)

$apriori->{minConfidence}=1.55; # The minimum confidence(percent, optional)

$apriori->{minLift}=1; # The minimum lift(optional)

$apriori->{minLeverage}=0; # The minimum leverage(optional)

$apriori->{minConviction}=0; # The minimum conviction(optional)

$apriori->{minCoverage}=0; # The minimum coverage(percent, optional)

$apriori->{output}=1;
# The output type (1 - Export to text file delimited by tab; 2 - Export to excel file with chart)(optional)

$apriori->{messages}=1; # A value boolean to display the messages(optional)

$apriori->{itemsKeyDescription}{'101'}='MILK'; # Hash table to add items by key and description
$apriori->{itemsKeyDescription}{102}='BREAD';
$apriori->{itemsKeyDescription}{'103'}='CEREAL';

my@items=(103,101);
$apriori->insert_key_items_transaction(\@items); # Insert key items per transaction
$apriori->insert_key_items_transaction([103,102]);
$apriori->insert_key_items_transaction([103,101,102]);
$apriori->insert_key_items_transaction([103,101,102]);
$apriori->insert_key_items_transaction([101,102]);
$apriori->insert_key_items_transaction([103,101,102]);
$apriori->insert_key_items_transaction([103,101,102]);
$apriori->insert_key_items_transaction([103,102]);
$apriori->insert_key_items_transaction([103,101,102]);
$apriori->insert_key_items_transaction([103,101,102]);

# or from a data file

# $apriori->input_data_file("datafile.txt",",");
# Insert items per line(transaction), accepts the arguments of path to data file and item separator

# file contents

103,101
103,102
103,101,102
103,101,102
101,102
103,101,102
103,101,102
103,102
103,101,102
103,101,102

print "\n${\$apriori->quantity_possible_rules}"; # Show the quantity of possible rules

$apriori->{limitRules}=10; # The limit of rules

$apriori->generate_rules;
# Generate association rules to no longer meet the minimum support, confidence, lift, leverage, conviction, coverage or limit of rules

print "\n@{$apriori->{frequentItemset}}\n"; # Show frequent items

#output messages

12
3 items, 12 possible rules
Large itemset size 2, 3 items
Processing...
Frequent itemset: { 103, 102, 101 }, 3 items
Exporting to excel "output_large_itemset_size_2.xlsx"...
Large itemset size 3, 3 items
Processing...
Frequent itemset: { 103, 101, 102 }, 3 items
Exporting to excel "output_large_itemset_size_3.xlsx"...
103, 101, 102

#output file "output_itemset_size_2.txt"

Rules	Support %	Confidence %	Lift	Leverage	Conviction	Coverage %
R1	70,00	77,78	1,11	7,00	0,10	90,00
R2	80,00	88,89	1,11	8,00	0,09	90,00
R3	70,00	87,50	1,25	14,00	0,08	80,00
R4	70,00	87,50	1,25	14,00	0,08	80,00
R5	80,00	88,89	1,11	8,00	0,09	90,00
R6	70,00	77,78	1,11	7,00	0,10	90,00

Rule R1: { 103 } => { 101 }
Support: 70,00 %
Confidence: 77,78 %
Lift: 1,11
Leverage: 7,00
Conviction: 0,10
Coverage: 90,00 %
Items:
103 CEREAL
101 MILK

to be continued...

#output file "output_itemset_size_3.txt"

Rules	Support %	Confidence %	Lift	Leverage	Conviction	Coverage %
R7	60,00	66,67	1,11	6,00	0,12	90,00
R8	60,00	85,71	1,43	18,00	0,07	70,00
R9	60,00	75,00	1,25	12,00	0,09	80,00
R10	60,00	75,00	1,25	12,00	0,09	80,00

Rule R7: { 103 } => { 101, 102 }
Support: 60,00 %
Confidence: 66,67 %
Lift: 1,11
Leverage: 6,00
Conviction: 0,12
Coverage: 90,00 %
Items:
103 CEREAL
101 MILK
102 BREAD

Rule R8: { 101, 103 } => { 102 }
Support: 60,00 %
Confidence: 85,71 %
Lift: 1,43
Leverage: 18,00
Conviction: 0,07
Coverage: 70,00 %
Items:
101 MILK
103 CEREAL
102 BREAD

to be continued...

# or from a database

# CREATE TABLE dimension_product(
	# product_key INTEGER NOT NULL PRIMARY KEY,
	# product_alternate_key INTEGER NOT NULL,
	# product_name TEXT NOT NULL,
	# price REAL NOT NULL
	# -- ...
# );

# INSERT INTO dimension_product VALUES(1,101,'MILK',10.00);
# INSERT INTO dimension_product VALUES(2,102,'BREAD',10.00);
# INSERT INTO dimension_product VALUES(3,103,'CEREAL',10.00);
# -- ...

# CREATE TABLE fact_sales(
	# sales_order_number INTEGER NOT NULL,
	# sales_order_line_number INTEGER NOT NULL,
	# product_key INTEGER NOT NULL,
	# quantity INTEGER NOT NULL,
	# -- ...
	# PRIMARY KEY(sales_order_number, sales_order_line_number),
	# FOREIGN KEY(product_key) REFERENCES dimension_product(product_key)
# );

# INSERT INTO fact_sales VALUES(1101,1,3,1);
# INSERT INTO fact_sales VALUES(1101,2,1,1);
# INSERT INTO fact_sales VALUES(1102,1,3,1);
# INSERT INTO fact_sales VALUES(1102,2,2,1);
# INSERT INTO fact_sales VALUES(1103,1,1,1);
# INSERT INTO fact_sales VALUES(1103,2,2,1);
# INSERT INTO fact_sales VALUES(1103,3,3,1);
# INSERT INTO fact_sales VALUES(1104,1,1,1);
# INSERT INTO fact_sales VALUES(1104,2,2,1);
# INSERT INTO fact_sales VALUES(1104,3,3,1);
# INSERT INTO fact_sales VALUES(1105,1,1,1);
# INSERT INTO fact_sales VALUES(1105,2,2,1);
# INSERT INTO fact_sales VALUES(1106,1,1,1);
# INSERT INTO fact_sales VALUES(1106,2,2,1);
# INSERT INTO fact_sales VALUES(1106,3,3,1);
# INSERT INTO fact_sales VALUES(1107,1,1,1);
# INSERT INTO fact_sales VALUES(1107,2,2,1);
# INSERT INTO fact_sales VALUES(1107,3,3,1);
# INSERT INTO fact_sales VALUES(1108,1,3,1);
# INSERT INTO fact_sales VALUES(1108,2,2,1);
# INSERT INTO fact_sales VALUES(1109,1,1,1);
# INSERT INTO fact_sales VALUES(1109,2,2,1);
# INSERT INTO fact_sales VALUES(1109,3,3,1);
# INSERT INTO fact_sales VALUES(1110,1,1,1);
# INSERT INTO fact_sales VALUES(1110,2,2,1);
# INSERT INTO fact_sales VALUES(1110,3,3,1);
# -- ...

use DBD::SQLite;
use Data::Mining::Apriori;

my $apriori = new Data::Mining::Apriori;

$apriori->{minSupport}=1.55;

$apriori->{minConfidence}=1.55;

$apriori->{minLift}=1;

$apriori->{minLeverage}=0;

$apriori->{minConviction}=0;

$apriori->{minCoverage}=0;

$apriori->{output}=1;

$apriori->{messages}=1;

my $db = DBI->connect('dbi:SQLite:dbname=DW.db','','');

my$sql = qq~
SELECT DISTINCT(fs.sales_order_number)
FROM dimension_product dp
JOIN fact_sales fs ON
dp.product_key = fs.product_key
-- WHERE ...
~;

my$query = $db->prepare($sql);
$query->execute;
my$transactions=$query->fetchall_arrayref;

foreach my$transaction(@$transactions){
	$sql = qq~
		SELECT dp.product_alternate_key, dp.product_name
		FROM dimension_product dp
		JOIN fact_sales fs ON
		dp.product_key = fs.product_key
		WHERE fs.sales_order_number = $$transaction[0];
		-- AND ...
	~;
	$query = $db->prepare($sql);
	$query->execute;
	my@items;
	while(my($key,$description)=$query->fetchrow){
		$apriori->{itemsKeyDescription}{$key}=$description;
		push@items,$key;
	}
	$apriori->insert_key_items_transaction(\@items);
}

print "\n${\$apriori->quantity_possible_rules}";

$apriori->{limitRules}=10;

$apriori->generate_rules;

print "\n@{$apriori->{frequentItemset}}\n";

DESCRIPTION

This module implements the apriori algorithm of data mining.

ATTRIBUTES

totalTransactions

The total number of transactions.

minSupport

The minimum support.(percent)

minConfidence

The minimum confidence.(percent, optional)

minLift

The minimum lift.(optional)

minLeverage

The minimum leverage.(optional)

minConviction

The minimum conviction.(optional)

minCoverage

The minimum coverage.(percent, optional)

limitRules

The limit of rules.(optional)

output

The output type:(optional)

1 - Text file delimited by tab;
2 - Excel file with chart.

messages

A value boolean to display the messages.(optional)

itemsKeyDescription

Hash table to add items by key and description.

itemsKeyTransactions

Reference to array, to add the transactions of each item per key.

frequentItemset

Frequent itemset.

associationRules

A data structure to store the name of the rule, key items, implication, support, confidence, lift, leverage, conviction and coverage.

	$self->{associationRules} = {
                                    '1' => {
                                                'R1' => {
                                                            'items' => [
                                                                         '103',
                                                                         '101'
                                                                       ],
                                                            'rule' => [
                                                                        '{ 103 } => { 101 }',
                                                                        '70,00',
                                                                        '77,78',
                                                                        '1,11',
                                                                        '7,00',
                                                                        '0,10',
                                                                        '90,00'
                                                                      ]
                                                        }
                                            },
                                            # to be continued...

METHODS

new

Creates a new instance of Data::Mining::Apriori.

insert_key_items_transaction(\@items)

Insert key items per transaction. Accepts the following arguments:

An array reference to key items.

input_data_file("datafile.txt",",")

Insert items per line(transaction). Accepts the following arguments:

Data file;
Item separator.

quantity_possible_rules

Returns the quantity of possible rules.

generate_rules

Generate association rules until no set of items meets the minimum support, confidence, lift, leverage, conviction, coverage or limit of rules.

association_rules

Generate association rules by size of large itemsets.

AUTHOR

Alex Graciano, <agraciano@cpan.org>

COPYRIGHT AND LICENSE

This library is free software; you can redistribute it and/or modify it under the same terms as Perl itself, either Perl version 5.12.4 or, at your option, any later version of Perl 5 you may have available.

To install Data::Mining::Apriori, copy and paste the appropriate command in to your terminal.

cpanm

cpanm Data::Mining::Apriori

CPAN shell

perl -MCPAN -e shell
install Data::Mining::Apriori

For more information on module installation, please visit the detailed CPAN module installation guide.

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