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->{metrics}{minSupport}=0.0155; # The minimum support (required), default value is 0.01 (1%)

$apriori->{metrics}{minConfidence}=0.0155; # The minimum confidence (required), default value is 0.10 (10%)

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

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

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

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

$apriori->{metrics}{minCorrelation}=0; # The minimum correlation (optional)

$apriori->{metrics}{minCosine}=0; # The minimum cosine (optional)

$apriori->{metrics}{minLaplace}=0; # The minimum laplace (optional)

$apriori->{metrics}{minJaccard}=0; # The minimum jaccard (optional)

$apriori->{precision}=2; # Sets the floating point precision of the metrics (required), default value is 3

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

$apriori->{pathOutputFiles}='data/'; # The path to output files (optional)

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

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

my@items=(103,101);
$apriori->insert_key_items_transaction(\@items); # Insert key items by 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 key items by line (transaction), accepts the arguments of path to data file and item separator

# file contents (example)

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 (optional)

$apriori->{limitSubsets}=12; # The limit of subsets (optional)

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

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

#output messages

12
3 items, 12 possible rules
Large itemset of length 2, 3 items
Processing ...
Frequent itemset: { 102, 103, 101 }, 3 items
Exporting to file data/output_large_itemset_length_2.txt ...
Large itemset of length 3, 3 items
Processing ...
Frequent itemset: { 101, 102, 103 }, 3 items
Exporting to file data/output_large_itemset_length_3.txt ...
101, 102, 103

#output file "output_itemset_length_2.txt"

Rules	Support	Confidence	Lift	Leverage	Conviction	Coverage	Correlation	Cosine	Laplace	Jaccard
R1	0,80	0,89	1,11	0,08	1,80	0,90	0,67	0,94	0,62	0,89
R2	0,70	0,78	1,11	0,07	1,35	0,90	0,51	0,88	0,59	0,78
R3	0,80	0,89	1,11	0,08	1,80	0,90	0,67	0,94	0,62	0,89
R4	0,70	0,78	1,11	0,07	1,35	0,90	0,51	0,88	0,59	0,78
R5	0,70	0,87	1,25	0,14	2,40	0,80	0,76	0,94	0,61	0,87
R6	0,70	0,87	1,25	0,14	2,40	0,80	0,76	0,94	0,61	0,87

Rule R1: { 102 } => { 103 }
Support: 0,80
Confidence: 0,89
Lift: 1,11
Leverage: 0,08
Conviction: 1,80
Coverage: 0,90
Correlation: 0,67
Cosine: 0,94
Laplace: 0,62
Jaccard: 0,89
Items:
102 BREAD
103 CEREAL

#...

#output file "output_itemset_length_3.txt"

Rules	Support	Confidence	Lift	Leverage	Conviction	Coverage	Correlation	Cosine	Laplace	Jaccard
R7	0,60	0,67	1,11	0,06	1,20	0,90	0,41	0,82	0,55	0,67
R8	0,60	0,75	1,25	0,12	1,60	0,80	0,61	0,87	0,57	0,75
R9	0,60	0,86	1,43	0,18	2,80	0,70	0,80	0,93	0,59	0,86
R10	0,60	0,67	1,11	0,06	1,20	0,90	0,41	0,82	0,55	0,67
R11	0,60	0,86	1,43	0,18	2,80	0,70	0,80	0,93	0,59	0,86
R12	0,60	0,75	1,25	0,12	1,60	0,80	0,61	0,87	0,57	0,75

Rule R7: { 102 } => { 101, 103 }
Support: 0,60
Confidence: 0,67
Lift: 1,11
Leverage: 0,06
Conviction: 1,20
Coverage: 0,90
Correlation: 0,41
Cosine: 0,82
Laplace: 0,55
Jaccard: 0,67
Items:
102 BREAD
101 MILK
103 CEREAL

Rule R8: { 102, 103 } => { 101 }
Support: 0,60
Confidence: 0,75
Lift: 1,25
Leverage: 0,12
Conviction: 1,60
Coverage: 0,80
Correlation: 0,61
Cosine: 0,87
Laplace: 0,57
Jaccard: 0,75
Items:
102 BREAD
103 CEREAL
101 MILK

#...

DESCRIPTION

This module implements the apriori algorithm of data mining.

ATTRIBUTES

totalTransactions

The total number of transactions.

metrics

The type of metrics

minSupport: The minimum support (required), default value is 0.01 (1%)
minConfidence: The minimum confidence (required), default value is 0.10 (10%)
minLift: The minimum lift (optional)
minLeverage: The minimum leverage (optional)
minConviction: The minimum conviction (optional)
minCoverage: The minimum coverage (optional)
minCorrelation: The minimum correlation (optional)
minCosine: The minimum cosine (optional)
minLaplace: The minimum laplace (optional)
minJaccard: The minimum jaccard (optional)

precision

Sets the floating point precision of the metrics (required), default value is 3

limitRules

The limit of rules (optional)

limitSubsets

The limit of subsets (optional)

output

The output type (optional):

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

pathOutputFiles

The path to output files (optional)

messages

A value boolean to display the messages (optional)

keyItemsDescription

Hash table reference to add item by key and description.

keyItemsTransactions

Hash table reference to add items by keys and transactions.

frequentItemset

Frequent itemset.

associationRules

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

$self->{associationRules} = {
							  '1' => {
									   'confidence' => '0.89',
									   'cosine' => '0.94',
									   'implication' => '{ 102 } => { 103 }',
									   'coverage' => '0.90',
									   'laplace' => '0.62',
									   'jaccard' => '0.89',
									   'support' => '0.80',
									   'correlation' => '0.67',
									   'items' => [
													'102',
													'103'
												  ],
									   'conviction' => '1.80',
									   'lift' => '1.11',
									   'leverage' => '0.08'
									 },
								#...

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.

# file contents (example)

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

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, correlation, cosine, laplace, jaccard or limit of rules.

association_rules

Generate association rules by length 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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