Research Article

Analysis of Traditional and Enhanced Apriori Algorithms in Association Rule Mining

by  Logeswari T, Valarmathi N, Sangeetha A, Masilamani M
journal cover
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 87 - Issue 19
Published: February 2014
Authors: Logeswari T, Valarmathi N, Sangeetha A, Masilamani M
10.5120/15457-3820
PDF

Logeswari T, Valarmathi N, Sangeetha A, Masilamani M . Analysis of Traditional and Enhanced Apriori Algorithms in Association Rule Mining. International Journal of Computer Applications. 87, 19 (February 2014), 4-8. DOI=10.5120/15457-3820

                        @article{ 10.5120/15457-3820,
                        author  = { Logeswari T,Valarmathi N,Sangeetha A,Masilamani M },
                        title   = { Analysis of Traditional and Enhanced Apriori Algorithms in Association Rule Mining },
                        journal = { International Journal of Computer Applications },
                        year    = { 2014 },
                        volume  = { 87 },
                        number  = { 19 },
                        pages   = { 4-8 },
                        doi     = { 10.5120/15457-3820 },
                        publisher = { Foundation of Computer Science (FCS), NY, USA }
                        }
                        %0 Journal Article
                        %D 2014
                        %A Logeswari T
                        %A Valarmathi N
                        %A Sangeetha A
                        %A Masilamani M
                        %T Analysis of Traditional and Enhanced Apriori Algorithms in Association Rule Mining%T 
                        %J International Journal of Computer Applications
                        %V 87
                        %N 19
                        %P 4-8
                        %R 10.5120/15457-3820
                        %I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, Enhanced Apriori Algorithm is proposed which takes less scanning time. It is achieved by eliminating the redundant generation of sub-items during pruning the candidate item sets. Both Traditional and Enhanced Apriori algorithms are compared and analysed in this paper.

References
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Index Terms
Computer Science
Information Sciences
No index terms available.
Keywords

Candidate generation frequent itemsets transaction_size support count threshold.

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