IEEE Transactions on Neural Networks and Learning Systems, ( ISI ), Volume (25), No (11), Year (2014-6) , Pages (2053-2064)

Title : ( Confabulation Inspired Association Rule Mining for Rare and Frequent Itemsets )

Authors: Azadeh Soltani , Mohammad Reza Akbarzadeh Totonchi ,

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A new confabulation-inspired association rule mining (CARM) algorithm is proposed using an interestingness measure inspired by cogency. Cogency is only computed based on pairwise item conditional probability, so the proposed algorithm mines association rules by only one pass through the file. The proposed algorithm is also more efficient for dealing with infrequent items due to its cogency-inspired approach. The problem of associative classification is used here for evaluating the proposed algorithm. We evaluate CARM over both synthetic and real benchmark data sets obtained from the UC Irvine machine learning repository. Experiments show that the proposed algorithm is consistently faster due to its one time file access and consumes less memory space than the Conditional Frequent Patterns growth algorithm. In addition, statistical analysis reveals the superiority of the approach for classifying minority classes in unbalanced data sets.

Keywords

, Association rule mining (ARM), associative classification, cogency, confabulation theory, rare item
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@article{paperid:1043602,
author = {Soltani, Azadeh and Akbarzadeh Totonchi, Mohammad Reza},
title = {Confabulation Inspired Association Rule Mining for Rare and Frequent Itemsets},
journal = {IEEE Transactions on Neural Networks and Learning Systems},
year = {2014},
volume = {25},
number = {11},
month = {June},
issn = {2162-237X},
pages = {2053--2064},
numpages = {11},
keywords = {Association rule mining (ARM); associative classification; cogency; confabulation theory; rare item mining},
}

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%0 Journal Article
%T Confabulation Inspired Association Rule Mining for Rare and Frequent Itemsets
%A Soltani, Azadeh
%A Akbarzadeh Totonchi, Mohammad Reza
%J IEEE Transactions on Neural Networks and Learning Systems
%@ 2162-237X
%D 2014

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