An efficient vertical-Apriori Mapreduce algorithm for frequent item-set mining

Algorithms such as OPUS and Apriori-based Mapreduce for enhancing the efficiency of mining frequent item-set for pattern recognition application from transactional dataset have been proposed in the literature. Most of these algorithms are, however, evaluated offline on relatively small data size. Wh...

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Vydáno v:2015 IEEE 10th Conference on Industrial Electronics and Applications (ICIEA) s. 108 - 112
Hlavní autoři: Dawei Sun, Lee, Vincent Cs, Burstein, Frada, Haghighi, Pari Delir
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.06.2015
Témata:
Apriori
attribute identifier
Big data
Dairy products
Data mining
Distributed databases
Frequent item-set mining
Generators
Itemsets
Mapreduce
Sugar
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Abstract Algorithms such as OPUS and Apriori-based Mapreduce for enhancing the efficiency of mining frequent item-set for pattern recognition application from transactional dataset have been proposed in the literature. Most of these algorithms are, however, evaluated offline on relatively small data size. When confronting with larger data size, which is inevitable for todays organisation, most if not all algorithms performed not as efficient as required to meet the real time big data driven decision making needs. We therefore attempt to solve these efficiency problems by proposing a VAMR (Vertical-Apriori Map-reduce) algorithm. VAMR is based on data attribute identifier which is exploited as capability metric for mining frequency item-set from large dataset in a single node (for example in a single site enterprise) that has no distributed and parallel computing system environment. Our evaluations using synthetic datasets and data from public repository suggest that VAMR algorithm can offer superior efficiency in mining frequent item-sets from large transaction dataset.
AbstractList Algorithms such as OPUS and Apriori-based Mapreduce for enhancing the efficiency of mining frequent item-set for pattern recognition application from transactional dataset have been proposed in the literature. Most of these algorithms are, however, evaluated offline on relatively small data size. When confronting with larger data size, which is inevitable for todays organisation, most if not all algorithms performed not as efficient as required to meet the real time big data driven decision making needs. We therefore attempt to solve these efficiency problems by proposing a VAMR (Vertical-Apriori Map-reduce) algorithm. VAMR is based on data attribute identifier which is exploited as capability metric for mining frequency item-set from large dataset in a single node (for example in a single site enterprise) that has no distributed and parallel computing system environment. Our evaluations using synthetic datasets and data from public repository suggest that VAMR algorithm can offer superior efficiency in mining frequent item-sets from large transaction dataset.
Author Haghighi, Pari Delir
Burstein, Frada
Lee, Vincent Cs
Dawei Sun
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  fullname: Haghighi, Pari Delir
  email: pari.delir.haghighi@monash.edu
  organization: Fac. of Inf. Technol., Monash Univ., Melbourne, VIC, Australia
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Snippet Algorithms such as OPUS and Apriori-based Mapreduce for enhancing the efficiency of mining frequent item-set for pattern recognition application from...
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StartPage 108
SubjectTerms Apriori
attribute identifier
Big data
Dairy products
Data mining
Distributed databases
Frequent item-set mining
Generators
Itemsets
Mapreduce
Sugar
Title An efficient vertical-Apriori Mapreduce algorithm for frequent item-set mining
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