Data::Mining::Apriori - Perl extension for implement the apriori algorithm of data mining.
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 #...
This module implements the apriori algorithm of data mining.
The total number of transactions.
The type of metrics
The minimum support (required), default value is 0.01 (1%)
The minimum confidence (required), default value is 0.10 (10%)
The minimum lift (optional)
The minimum leverage (optional)
The minimum conviction (optional)
The minimum coverage (optional)
The minimum correlation (optional)
The minimum cosine (optional)
The minimum laplace (optional)
The minimum jaccard (optional)
Sets the floating point precision of the metrics (required), default value is 3
The limit of rules (optional)
The limit of subsets (optional)
The output type (optional):
1 - Text file delimited by TAB;
2 - Excel file with chart.
The path to output files (optional)
A value boolean to display the messages (optional)
Hash table reference to add item by key and description.
Hash table reference to add items by keys and transactions.
Frequent itemset.
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' }, #...
Creates a new instance of Data::Mining::Apriori.
Insert key items per transaction. Accepts the following arguments:
An array reference to key items.
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
Returns the quantity of possible 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.
Generate association rules by length of large itemsets.
Alex Graciano, <agraciano@cpan.org>
Copyright (C) 2015-2018 by Alex Graciano
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.