Research Article

A Suitability Study of Discretization Methods for Associative Classifiers

by  O. P. Vyas, Kavita Das
journal cover
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 5 - Issue 10
Published: August 2010
Authors: O. P. Vyas, Kavita Das
10.5120/944-1322
PDF

O. P. Vyas, Kavita Das . A Suitability Study of Discretization Methods for Associative Classifiers. International Journal of Computer Applications. 5, 10 (August 2010), 46-51. DOI=10.5120/944-1322

                        @article{ 10.5120/944-1322,
                        author  = { O. P. Vyas,Kavita Das },
                        title   = { A Suitability Study of Discretization Methods for Associative Classifiers },
                        journal = { International Journal of Computer Applications },
                        year    = { 2010 },
                        volume  = { 5 },
                        number  = { 10 },
                        pages   = { 46-51 },
                        doi     = { 10.5120/944-1322 },
                        publisher = { Foundation of Computer Science (FCS), NY, USA }
                        }
                        %0 Journal Article
                        %D 2010
                        %A O. P. Vyas
                        %A Kavita Das
                        %T A Suitability Study of Discretization Methods for Associative Classifiers%T 
                        %J International Journal of Computer Applications
                        %V 5
                        %N 10
                        %P 46-51
                        %R 10.5120/944-1322
                        %I Foundation of Computer Science (FCS), NY, USA
Abstract

Discretization is a popular approach for handling numeric attributes in machine learning. The attributes in the datasets are both nominal and continuous. Most of the Classifiers are capable to be applied on discretized data. Hence, pre-processing of continuous data for converting them into discretized data is a necessary step before being used for the Classification Rule Mining approaches. Recently developed Associative Classifiers like CBA, CMAR and CPAR are almost equal in accuracy and have outperformed traditional classifiers. The distribution of continuous data into discrete ranges may affect the accuracy of classification. This work provides a comparative study of few discretization methods with these new classifiers. The target is to find some suitable discretization methods that are more suitable with these associative classifiers.

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

ARM CRM CBA CMAR CPAR CADD USD ChiMerge MDLP ID3 EWD EFD

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